A Model for Sustainable Quality Control Improvement in the Foundry Industry Using Key Performance Indicators

Abstract

The foundry industry consumes significant amounts of natural resources, metals, and energy, and it generates large amounts of solid waste and gases, which have a significant impact on the environment. Therefore, taking sustainability-based improvement measures in foundry companies is necessary and an important part of sustainable development for humanity. The aim of this study was to develop a universal indicator model for quality control improvement focused on the foundry industry. The model allows a multi-criteria analysis of various quality control methods and the determination of their gradation in the context of ensuring an objectively high level of product quality. A test of the model carried out in foundry companies confirmed its suitability. An optimisation of the relationship between product quality and quality control efficiency was carried out, which fulfilled the criteria of efficiency, reliability, low emissivity, low energy intensity, low cost, short lead time, and automation. Thanks to the indicated features, the model clearly fits into the concept of sustainable development and Industry 4.0. The result of the realised research, i.e., the ranking of the gradation of detection methods, allowed optimisation of quality control within the analysed production process. Future research directions will address the integration of digital solutions within the model.

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Academic Editors: Georgios
K. Koulinas and Dimitrios
E. Koulouriotis
Received: 20 January 2025
Revised: 3 February 2025
Accepted: 7 February 2025
Published: 9 February 2025
Citation: Czerwi´nska, K.; Pacana,
A.; Ostasz, G. A Model for
Sustainable Quality Control
Improvement in the Foundry
Industry Using Key Performance
Indicators. Sustainability 2025,17,
1418. https://doi.org/10.3390/
su17041418
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Article
A Model for Sustainable Quality Control Improvement in the
Foundry Industry Using Key Performance Indicators
Karolina Czerwi ´nska 1, Andrzej Pacana 1, * and Grzegorz Ostasz 2
1Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology,
Al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland; k.czerwinska@prz.edu.pl
2Faculty of Management, Rzeszow University of Technology, Al. Powstancow Warszawy 12,
35-959 Rzeszow, Poland; gost@prz.edu.pl
*Correspondence: app@prz.edu.pl; Tel.: +48-178651390
Abstract:
The foundry industry consumes significant amounts of natural resources, metals,
and energy, and it generates large amounts of solid waste and gases, which have a sig-
nificant impact on the environment. Therefore, taking sustainability-based improvement
measures in foundry companies is necessary and an important part of sustainable devel-
opment for humanity. The aim of this study was to develop a universal indicator model
for quality control improvement focused on the foundry industry. The model allows a
multi-criteria analysis of various quality control methods and the determination of their
gradation in the context of ensuring an objectively high level of product quality. A test of
the model carried out in foundry companies confirmed its suitability. An optimisation of
the relationship between product quality and quality control efficiency was carried out,
which fulfilled the criteria of efficiency, reliability, low emissivity, low energy intensity, low
cost, short lead time, and automation. Thanks to the indicated features, the model clearly
fits into the concept of sustainable development and Industry 4.0. The result of the realised
research, i.e., the ranking of the gradation of detection methods, allowed optimisation of
quality control within the analysed production process. Future research directions will
address the integration of digital solutions within the model.
Keywords:
sustainability industry; Industry 4.0; mechanical engineering; quality
management; quality control system; KPI; indicator analysis
1. Introduction
As a result of the ongoing technological, economic, environmental, and social trans-
formations, we are seeing turbulent changes in the environment relevant to the operation
of manufacturing enterprises. The changing environment of enterprises generates a
significant number of challenges in many spheres (technological, social, economic, and
environmental) [
1
,
2
]. In a global economy, dynamically transforming markets force
organizations to develop the ability to anticipate market needs. Today, companies pay
attention to the increase in the quality of manufactured products and the growth of
knowledge, skills, and innovation [
3
,
4
]. Accordingly, modern technology and innovation
can be a source of significant competitive advantage. By achieving significant progress
in innovation and applying accumulated knowledge, companies are trying to determine
the next direction of development and build new strategies and models for managing
production processes [5–7].
In the field of enterprise improvement, the development strategy plays an impor-
tant role according to the assumptions of the concept of Industry 4.0. Knowledge-based
Sustainability 2025,17, 1418 https://doi.org/10.3390/su17041418

Sustainability 2025,17, 1418 2 of 34
(innovative) companies, in a much more effective way than competition, increase their
position in the market [
8
]. Achieving a stable position in many cases can be a strategic
goal of the company’s operation. Technological development, especially the use of digital
technology, enables more efficient management of production processes and their intensity
of energy. Manufacturing companies are inclined to implement solutions derived from the
assumptions of Industry 4.0 (the fourth industrial revolution) [
9
–
11
]. Industry 4.0 means
the formation of intelligent value chains using dynamic and optimising socio-technical
systems bearing the name of smart factories [
12
,
13
]. They are formed by a production envi-
ronment in which machines, production facilities, and logistics systems communicate with
each other and organize the production process largely autonomously [
14
]. Smart factories
are less energy-intensive and at the same time more productive, efficient, and sustainable
than traditional manufacturing factories. They can produce higher-quality products at a
lower cost and in a shorter time and respond more quickly to changes in demand [
15
–
17
].
Industrial automation leads to significant increases in efficiency and productivity [18].
The literature on this topic points to progressive and profound changes in industries
related to the automation of processes and the successive interconnection of production
systems, which consolidates many planes of company operation [
19
–
21
]. Issues related to
Industry 4.0, its definitions, assumptions, and goals have been presented in detail in the
literature [
22
–
24
]. Furthermore, studies can be found related to the changing role of the
employee as a consequence of innovation in companies [
25
–
27
] as well as the benefits of
automation in the area of product quality management [
28
,
29
] and the integration of lean
concepts with Industry 4.0 [30].
Quality control automation is a modern approach that uses advanced technologies
(robotics [
31
], artificial intelligence [
32
], and vision systems [
33
]) to increase the efficiency,
precision, and reliability of inspection processes [
34
,
35
] with optimized use of electricity.
Automated quality control systems can perform detection with high speed and accuracy
and in a sustainable manner, detecting nonconformities more efficiently than traditional
inspection [
36
–
38
]. Automating quality control not only improves the accuracy and consis-
tency of quality assessments but also reduces the time and costs associated with manual
inspections [
39
,
40
]. Advanced technologies such as artificial intelligence have the ability to
analyse data in real time, predict potential problems, and suggest corrective actions [
41
–
43
].
Companies that implement quality control automation can gain competitive advantage
and customer loyalty by offering products of higher quality while reducing costs and
production time [44,45].
However, it should be noted that the development of industry, beginning with
the first industrial revolution, has had certain economic, social, and environmental
consequences [
46
,
47
]. As a result of the development of technology, opportunities to
change and transform the world have increased, but not all aspects of technical and
scientific development in general have brought clear benefits to humanity. Acute changes
in natural capital are an example [
48
,
49
]. Based on the unbalanced relationship between
the economic, social, and environmental spheres, the idea of sustainable and balanced
development was born [
50
,
51
]. All three spheres intermingle and create sustainable
development [
52
]. Analysing the environmental aspect, the key tasks include reducing
the adverse impact of human activity on the environment and minimising the negative
impact of economic development on the environment [
53
,
54
]. The literature points out
that environmental degradation and pollution (through economic development) should
not be left to the market; they should be subject to institutional regulation at the state
and international levels [55,56].
The economic sustainability of companies means production that meets consumer
demand without consequences for future generations [
57
]. It also means undertaking

Sustainability 2025,17, 1418 3 of 34
projects that are orientated toward the fundamental economic goal and complementing it
with close attention to environmental and social aspects (as the main areas of sustainabil-
ity) [
58
,
59
]. Corporate sustainability is often equated with the green development of the
economy, the foundation of which is sustainable production and consumption [
60
]. The
literature identifies the determinants of sustainable development in the context of digital
transformation [
61
]. The essence of modernisation of production is emphasized, leading to
optimization of processes, reduction in energy and materials, and the effective dialogue
between the company and the environment [
62
]. It is important for companies to take
responsibility for their actions, stay ahead of innovation, use resources efficiently, reduce
harmful emissions, improve quality and working conditions, and at the same time take
care of the external environment [63,64].
In the context of the foundry industry, a particular consequence for future generations
could be the depletion of the raw material base, which is already being strained by rapid
global economic growth [
65
]. The technical plane, which is characterised by degradation
and a high level of energy intensity, is of particular importance in enterprises, even those
that are significantly automated [66]. Analyses and improvements in material and energy
absorption should have automatic and semiautomatic quality control in addition to man-
ufacturing processes. Manufacturing of cast products and ensuring an acceptable level
of quality is conditioned by a considerable number of technological parameters [
67
,
68
].
The main difficulty that arises in the casting process is the impossibility of simultaneously
inspecting all relevant factors [
69
]. With this in mind, usually, inspection of cast products
begins with reference to detection of the raw alloy [
70
,
71
]. Good knowledge of the melting
process of specific alloys makes it possible to plan and execute effective quality control that
ensures a tight detection system [
72
]. The selection of the implementation of quality control
(quality control methods and tools) is made on the basis of various criteria, depending on
the stakeholders [73].
A review of the literature indicates that any improvement activities implemented
in the manufacturing sphere should be created by increasing the efficiency of processes
(often closely related to automation and digitisation: the concept of smart factories) in
accordance with the idea of sustainable development [74,75]. In the era of climate change,
it is extremely important to implement measures to support sustainability in the industrial
space not only at the macro level but also at the micro level [76].
The consumption of electricity needed to produce the castings is largely dependent on
their individual characteristics and the equipment of a given foundry as well as the type
of quality control methods used [
77
,
78
]. Its reduction is possible by optimising the way
castings are produced in such a way that technological excesses are reduced to the necessary
minimum (technology improvement), the smallest possible number of non-conforming
products is generated (process implementation improvement), and effective detection of
non-conformities is carried out (quality control improvement) [
79
]. At the same time,
the type and technical condition of the equipment used in the production line is an
important factor. Ultimately, the reduction in energy intensity is reflected in the price of
the final product, as lowering production costs while maintaining good quality increases
the competitiveness of companies in the market. Today, the modern foundry industry is
paying more and more attention to the problem of the energy intensity of production
processes, and reducing energy consumption will allow even better economic results in
the future [80–82].
Given the continuous progress in the technical plane of manufacturing enterprises, the
introduction of the premises of sustainable development, and the concept of Industry 4.0,
development should also occur in the area of product quality control management [
83
].
Quality control is one of the critical processes in which a product is evaluated and consid-

Sustainability 2025,17, 1418 4 of 34
ered acceptable or rejected [
84
,
85
]. An effective quality control system is considered to be
detection, carried out in a semi-automatic or fully automated manner having an adequate
level of the following characteristics: high detection efficiency and high level of reliability
and at the same time a relatively low level of energy consumption, emission, time con-
sumption, and cost consumption. The key features of quality control methods are closely
related not only to the concept of smart factories, but they also correlate with the concept
of sustainable development. In addition, the key features of quality control methods are
interconnected and are all extremely important for success. An effective quality control
system by its scope includes control points located within a specific production process.
Analyses of quality control methods often reveal significant savings potential within the
cited features, which ultimately contributes to providing a tight quality control system that
reduces production costs.
A good way to measure the features of quality control methods is to use indicator
analysis, which is an in-depth analysis of the data made during multifaceted analysis.
Indicator analysis allows control of the functioning of the company. It allows one to see
to what extent the results of activities are in line with the goals set and whether they lead
to their realisation [
86
]. Quality control in the foundry industry is characterised by an
increasing amount of numerical data collection. Consequently, the data must be processed
and handled in a skilful manner, which characterises Industry 4.0. Drawing knowledge
and drawing constructive conclusions from the collected data is a challenge faced by all
developing companies.
A review of the available literature on the subject reveals that the issue of improving
the quality control of cast-aluminium alloy products using semiautomatic and automatic
inspection methods combined with an environmentally friendly approach is an issue
that is poorly recognised in practice. However, there are publications describing changes
in production processes related to sustainability, most often referred to as clean pro-
duction [
87
–
89
] and inspection improvement through automation [
90
,
91
], but within
these, there is a lack of attempts to diagnose and confirm with practice changes related
to sustainable quality control improvement. An overview of the proposals available in
the literature to improve the detection of aluminium castings is presented in Table 1.
The literature on the subject indicates that work is underway to improve quality
control in the foundry industry. Care for proper casting quality begins at the initial stage
of the process, including observing proper practices in the sand casting process. It has
been shown from casting defects and their remedies that the level of non-conformities can
be reduced by controlling the moulding parameters (sand grain size, clay content, and
moisture percentage) [
92
]. Controlling the flow of molten metal by maintaining predefined
levels or level patterns is one of many powerful tools to achieve this. The authors also point
out the testing of molten liquid metal using sensors. This is important because alloy quality
determination techniques often require laboratory analysis of samples taken from the
molten metal. In addition to reducing processing efficiency, the delays associated with this
laboratory analysis can lead to uncertainty, as the conditions of the alloy can change when
held at high temperatures. Therefore, online alloy quality monitoring is beneficial, as it
provides faster feedback to improve process control [
93
]. Test benches are being developed
for the development of advanced multi-sensor quality control and sorting systems based
on a software system for particle detection, segmentation, and classification [
94
]. Research
is also being conducted on the use of infrared thermography to assess the quality of liquid
metal [
84
]. Applications are being developed to measure the level of molten nonferrous
metal and control the flow of molten metal [
95
]. The literature also indicates the use of
deep learning in the quality control of aluminium castings for detection of defects [
96
] and
metal filings on cast-aluminium parts [
97
]. Deep learning methods also support NDT (non-

Sustainability 2025,17, 1418 5 of 34
destructive testing) methods, in which object detection methods based on deep learning are
trained and integrated using a dataset of RTG images of aluminium parts to detect internal
defects and predict their types without human attention [
98
]. Research is also underway to
improve the visual inspection of the cast parts. Proprietary automated methods that use
redundant views of the test object are being developed to perform the inspection [
99
] as
well as new laser-assisted optical inspection methods for high-pressure aluminium-cast
products [100].
Table 1. Characteristics of the activities undertaken to improve the control of aluminium castings.
Control
Stage Activity Characteristics Author
Checking
before
casting
Experimental
investigation of casting
defects in
aluminium castings
Improvement following proper practices in the sand
casting process. It was discovered from casting defects and
their remedies that defects can be minimised by controlling
the quality of the moulding sand, such as sand grain size,
clay content, and moisture percentage.
Kandpal
et al. [92]
Quality control of liquid aluminium alloy
Use of sensors to
monitor the quality of
molten aluminium
Research into online alloy quality monitoring that provides
faster feedback to improve process control. The paper
discusses techniques for monitoring important aspects of
alloy quality, in particular chemical composition, gas
content, and inclusion content.
Fergus [93]
Research was conducted on a test bench for the
development of advanced multi-sensor quality control and
sorting systems. This is based on a software system for
particle detection, segmentation, and classification.
Induction of the high-speed detection and sorting of
non-ferrous metal alloys, including cast and wrought
aluminium alloys.
De Joung
et al. [94]
Application of thermal
imaging cameras for
quality control of liquid
aluminium alloy
Using infrared thermography to assess the quality of a
liquid metal. The crystallisation process of the alloy was
investigated by TDA using a crystaldigraph device and an
Optris PI thermal imaging camera.
Wladysiak
[84]
Automatic melt flow
control to improve
casting parameters
Controlling the flow of molten metal by maintaining
predefined levels or level standards is one of many
powerful tools to achieve this goal. Precimeter Control
specialises in non-ferrous molten-metal level measurement
and molten metal flow control applications.
Jarlsmark
et al. [95]
Control of the cast product
Application of deep
learning in
quality control
Detection of surface defects in aluminium alloy castings
based on data enrichment and CRT-DETR. In a method for
detecting surface defects in aluminium alloy castings based
on deep learning, a surface defect detection model based
on data enhancement and a casting real-time detection
transformer were designed.
Li et al. [96]
A comparison of four deep learning models: Faster
R-CNN, RetinaNet, YOLOv7 and YOLOv7-tiny, to find out
which one is more suitable for quality assurance in metal
filings detection on cast-aluminium parts.
Nascimento
et al. [97]
Detection of defects in
aluminium castings and
their types based on
deep learning
In the study, state-of-the-art object detection methods
based on deep learning were trained using an X-ray image
dataset of aluminium parts to detect internal defects and
predict their types without human attention. The Al-cast
image dataset used in this study contains 3466 images of
parts produced on high-pressure die-casting machines.
Parlak
et al. [98]

Sustainability 2025,17, 1418 6 of 34
Table 1. Cont.
Control
Stage Activity Characteristics Author
Automated multi-view
inspection of
metal castings
A proposal for a proprietary method, Automated Multiple
View Inspection, that uses redundant views of a test object
to perform an inspection task. This method opens up new
possibilities in the field of inspection, taking into account
useful information about the correspondence between
different views. It is very robust because it identifies
potential defects in each view in a first step, and in a
second step, it finds correspondences between potential
defects, and only those that are matched in different views
are detected as actual defects.
Mery
et al. [99]
Laser-assisted optical
inspection of
high-pressure
aluminium-
cast products
The new method aims to improve on the previous practice
of checking castings only visually for surface defects such
as laminations, underfills, and cold streams. The method is
based on the principle of laser triangulation. The measured
point cloud is analysed using software specifically
designed to automatically detect surface defects.
Gruden
et al. [100]
Recent theoretical developments and their practical implications indicate that, to
improve the quality control of aluminium castings, researchers are focussing on defect
detection by NDT methods that are integrated with deep learning. The area of recent
developments is characterised in Table 2.
Table 2.
Characterisation of recent research areas in the improvement of aluminium casting inspection.
Step Activity Characteristics Author
Control of the cast product
Method for the detection of
deep objects and simulated
ellipsoidal defects
Evaluation of eight state-of-the-art deep object
detection methods (based on YOLO, RetinaNet,
and EfficientDet) that are used to detect defects in
aluminium castings. Proposition of a training
strategy that uses a small number of defect-free
X-ray images of castings with overlay of simulated
defects (avoiding manual annotation).
Mery [101]
Detection of defects in
aluminium castings and
their types based on
deep learning
The most modern methods based on deep learning
have been trained using a dataset of X-ray images
of aluminium parts to detect internal defects and
predict their types without human attention.
Parlak et al. [98]
Images of metal castings were captured using an
automated camera and then pre-processed to
remove noise from the images using a Gauss filter.
Various Harris, Otsu, Hough, and Canny feature
extraction algorithms were used to extract various
topographic features, including corners, contours,
discontinuities, and edges. Two models, the
Support Vector Machine (SVM) and K-Nearest
Neighbour (KNN) models, were trained on
topographic features extracted from more than
1400 images of aluminium castings.
Yousef et al. [102]

Sustainability 2025,17, 1418 7 of 34
Table 2. Cont.
Step Activity Characteristics Author
A model for automatic defect
detection in X-ray images of
aluminium castings using
deep learning and adaptive
Retinex multiscale
enhancement.
The Gain-Adaptive Multi-Scale Retinex (GAMSR)
algorithm is designed to improve raw X-ray data with
low contrast and noise. To address the problem of
omitting small pinhole defects during detection, the
feature pyramid network (FPN) was combined with the
Convolutional Block Attention Module (CBAM) to
extract high-level semantic information from X-ray
images.
Hai et al. [103]
Predicting casting quality
A self-learning and improved
approach to predicting the
quality of aluminium alloy
castings based on XGBoost.
A model that proposes targeted quality control to
achieve high responsiveness and low operating costs.
The model suggests that casting machine manufacturers
integrate advanced quality prediction features into the
next generation of intelligent casting machines.
Wang et al. [104]
Prediction of porosity defects
in aluminium alloys using
convolutional neural
networks (CNN).
Building a CNN model that can accurately predict
porosity defects in optical microscopy images. Nikolic et al. [105]
The latest developments in improving the quality control of aluminium castings, pre-
sented in Table 2, are closely related to the concept of Industry 4.0 through significant
automation and online reading of results. The research conducted indicates that the most
common implication of machine learning relates to NDT, i.e., X-ray inspection. However,
there is a lack of close reference to the idea of sustainability. The literature on the subject
indicates that improving the quality control of aluminium castings in the context of envi-
ronmental care (reducing the negative impact of the process) is an area that has been less
explored. The work undertaken in this area is concerned with the creation of proprietary
models that are most often applied across companies and simultaneously analyse a set of
eight environmental problems, including human toxicity, abiotic depletion, global warming,
solid waste production, acidification, terrestrial ecotoxicity, photochemical ozone forma-
tion, and aquatic toxicity, caused by the aluminium casting plant [
106
]. Models are also
being developed to analyse scenarios to reduce the environmental impact of an aluminium
die-casting plant [107].
A review of the work relating to the improvement of quality control of aluminium
castings shows that the authors focus on improving individual detection methods and ap-
proaches rather than optimising the entire quality control implemented as part of a specific
production process (locating quality control points with a view to their effectiveness). On
the basis of the analysis, it can be observed that there is a research gap. To fill it, a model of
an effective quality control system based on indicator analysis was developed.
Based on the analysis and consideration of the literature on the subject and with
reference to the identified research gaps, a model for the implementation and maintenance
of an effective quality control system for cast-aluminium alloy products was developed. The
originality aspect of the presented model manifests itself in the presented course of action,
which leads to multifaceted indicator analyses of quality control and its improvement.
The model subjected to indicator analysis a number of key features (for manufacturing
companies and the environment) of quality control points, which relate to four main
dimensions: the economic dimension (efficiency, reliability, cost, and time), environmental
and social dimension (emissivity and energy intensity), modernity dimension (automation
and digitisation), and image dimension (customer loyalty and company competitiveness).
At the same time, the main premise of the diagnosis and subsequent improvement is to
take care of the implementation of improvement activities in accordance with the concept

Sustainability 2025,17, 1418 8 of 34
of sustainable development and in the direction of Industry 4.0 (smart factory). A detailed
analysis of quality control is a translation of microscale activities that have a significant
impact while simultaneously ensuring the appropriate level of quality of the products
offered, taking care of the pro-environmental nature of the product and the activities
implemented, with improvements closely related to automation and digitisation. The
presented multifaceted activities show the superiority of the developed model over the
classical approach to quality control, which mostly deals with the analysis of product
non-conformities alone or the implications of new quality control methods. Extended
analyses make it possible to determine the total efficiency of individual quality control
points and develop an improvement strategy that simultaneously ensures the quality
stability of products, reduces the negative impact of detection on the environment, and
introduces full automation and digitisation of detection. The presented model fills the
research gaps detected in the literature on building systems for effective quality control
of production processes in the context of the implications of the ideas of sustainable
development and Industry 4.0.
A model that ensures an effective quality control system must increase the effectiveness
of the application of quality control in the form of the NDT used in the detection of
aluminium products. The model makes it possible to optimise the implementation of
individual quality control methods in terms of their location within the production process
and the frequency of application of NDT, i.e., to specify the optimum size and frequency of
sampling. This is made possible by collecting data on the quality control points of a specific
production process and then applying a set of indicators indicating such characteristics
as effectiveness, reliability, energy intensity, emissions, cost, and time intensity. Partial
data on the analysed quality control points (i.e., frequency of detection method within the
production process, frequency of identification of nonconformities within the production
process, failures of detection machines and equipment, energy intensity of unit detection,
emissivity of unit detection (chemicals, waste, and pollutants), time of unit detection, cost
of unit detection, and types of nonconformities detected with the use of individual NDT
methods) make it possible to determine the level of effectiveness of individual methods.
This allows us to plan and execute improvement measures. The model’s assumptions
indicate that improvement activities should be in line with the concept of sustainability
and steer the company towards Industry 4.0.
The role of Industry 4.0 and sustainability in the analyses carried out in accordance
with the model presented involves activities that touch upon the social, economic, and
environmental dimensions while leading to the implication of automation and digital
solutions directing the company towards Industry 4.0, which alludes to the specific ob-
jectives of the study. The model addresses the analysis of automated and semiautomated
quality control, pointing to its superiority over manual-performed NDT. Carrying out
automated or semi-automated quality control improves the efficiency of both the detection
and management of resources and logistical processes, resulting in lower consumption
of raw materials, energy, and water as well as reducing testing time. Using KPIs (key
performance indicators), it is possible to quantify the level of benefit from automation.
Automation in the NDT testing of aluminium castings supports sustainability, leading to
a reduction in waste and carbon footprint while increasing the efficiency of the overall
process. The model test confirms that automation of quality control in the casting industry
helps to improve ESG (Environmental, Social, and Corporate Governance) activities and
allows the benefits of sustainable development to be reaped. Determining the efficiency
level of individual detection methods enables precise resource management, resulting in
less waste.

Sustainability 2025,17, 1418 9 of 34
The overall objective was to develop a model for the implementation and maintenance
of an effective quality control system based on indicator analysis, taking into account
the principles of sustainable development and the concept of Industry 4.0. The specific
objective was to demonstrate the superiority of automated control over semi-automated
control in terms of the number of benefits and to demonstrate the close relationship between
automated quality control of aluminium castings and the idea of sustainable development
and Industry 4.0. The model aims to facilitate the production of cast-aluminium alloy
products within which automated or semiautomated quality control is carried out. The
method allows simultaneous analysis of all quality control points located within a specific
production process. The method uses indicator analysis to determine the partial efficien-
cies of detection methods taking into account their key characteristics, such as efficiency,
reliability, energy intensity, emissivity, cost intensity, and time intensity. This procedure
allows one to identify the level of effectiveness of the total methods used for quality control
within checkpoints and plan appropriate improvement measures. Given the fact that
the competitiveness of manufacturing companies is largely based on offering products
of adequate quality and on intangible resources (creativity, knowledge, and partner rela-
tionships), the issue of indicator-based improvement of the quality control of aluminium
castings in accordance with the concepts of sustainable development and Industry 4.0 can
be considered a legitimate subject of research and scientific consideration.
2. Model of an Effective Quality Control System
2.1. Assumptions for Building an Effective Quality Control System
An effective quality control system is the foundation of any enterprise seeking to
ensure the high quality of its products or services while maintaining a rational level of
electricity use. The model of an effective quality control system was constructed on the
basis of the concept of sustainable development and the premises of Industry 4.0. The
model assumes the development, automation, and multifaceted improvement of quality
control methods in the context of their impact on the environmental, economic, and social
dimensions and consequently on the image dimension of the manufacturing enterprise.
Measurement of the progress is carried out with the use of key performance indicators,
which significantly supports decision-making processes for ensuring the high quality of
manufactured products, provided services, and implemented production processes.
The model of an effective quality control system presented is based on awareness
and responsibility for actions that affect the economy, economy, environment, and society
with regard to the control methods used. The model takes into account the diagnostics of
products at various stages of production using sequentially integrated diagnostic testing
(non-destructive testing), which ultimately leads to internal and external benefits for the
company. The developed model takes into account all quality assurance control points
located within the analysed manufacturing process. The model is characterised by mul-
tifaceted analysis and is universal in relation to the subject of research, aluminium alloy
products. The assumptions of the concept of proceeding in the context of building an
effective quality control system for aluminium castings are illustrated in Figure 1.
Quality assurance is an important part of production and service management and
is intended to ensure that products comply with certain quality standards. The model
developed (Figure 1) considers an effective control system for aluminium castings as a
system characterised by the following features: integrated, efficient, stable, supervised,
trouble-free, systematic in its activities, and energy-efficient. The quality control process
involves monitoring, testing, and inspecting products at various stages of production
to detect and correct defects before finalising the product. Quality detection should be
low-energy and integrated into the entire production or service process, which means that

Sustainability 2025,17, 1418 10 of 34
every step from design to delivery of the final product should be monitored for quality
and energy consumption. The importance of an effective quality control system lies in
preventing problems before they reach customers, which reduces the costs associated with
repair, replacement, and complaint handling, and optimising quality control methods
with the relationship between product quality and optimizing the level of electricity use.
Quality control also helps identify areas for improvement in production processes, leading
to continuous improvement in areas related to automation (autonomous process), digital
solutions (integration of machines and systems in the company), products, services, techno-
logical processes, working conditions, and the environment. The model in its assumptions
emphasises the importance of regular training of employees so that they have the necessary
skills and knowledge to perform quality control tasks. This indicates that undertaking
changes should take into account maintaining quality and be oriented towards people, the
environment, and the automation of detection activities and, in addition, should lead to the
rationalisation of the use of raw materials.
Sustainability 2025, 17, x FOR PEER REVIEW 10 of 36
Figure 1. Concept of an effective quality control system for aluminium castings.
An effective quality control system includes a set of procedures, standards, tools, and
quality detection methods that are used to monitor, evaluate, and improve quality at all
stages of production or service provision. Relative to the products targeted by the model
(aluminium products), in most cases, quality control is practised using non-destructive
testing (NDT), which is semi-automatic or automatic. Quality control automation is a
modern approach to quality management that uses advanced technologies, such as robot-
ics or vision systems, to increase the precision, efficiency, and reliability of inspection pro-
cesses. Automated and semi-automated detection systems can perform inspections with
high speed and accuracy, identifying defects and nonconformities. This makes non-de-
structive testing an extremely important tool in the field of quality control, allowing as-
sessing the condition of materials and structures without destroying them. Therefore, in
the developed model, the monitoring of changes within key features is concerned with
guiding the level of effectiveness and efficiency of NDT methods within the analysed
Figure 1. Concept of an effective quality control system for aluminium castings.

Sustainability 2025,17, 1418 11 of 34
An effective quality control system includes a set of procedures, standards, tools, and
quality detection methods that are used to monitor, evaluate, and improve quality at all
stages of production or service provision. Relative to the products targeted by the model
(aluminium products), in most cases, quality control is practised using non-destructive
testing (NDT), which is semi-automatic or automatic. Quality control automation is a
modern approach to quality management that uses advanced technologies, such as robotics
or vision systems, to increase the precision, efficiency, and reliability of inspection processes.
Automated and semi-automated detection systems can perform inspections with high
speed and accuracy, identifying defects and nonconformities. This makes non-destructive
testing an extremely important tool in the field of quality control, allowing assessing the
condition of materials and structures without destroying them. Therefore, in the developed
model, the monitoring of changes within key features is concerned with guiding the level
of effectiveness and efficiency of NDT methods within the analysed production process
using key indicators. The rational deployment of detection methods leads to a reduction in
power consumption while maintaining the quality of products.
The key indicators for monitoring an effective quality control system used in the
model provide information that proves the condition of the economic, environmental,
and social dimensions as well as automation, thus indicating the assumptions of the
concept of sustainable development and Industry 4.0. The analysed features classified
into individual dimensions are the result of the needs, priorities, aspirations, and views of
various stakeholder groups (internal and external), selected on the basis of determining the
level of influence and interest. Regular monitoring of individual quality detection points
in the context of variables affecting the economic, environmental, and social dimensions
makes it possible to determine their level of effectiveness.
The development and implication of effective improvement activities in the provision
of an effective quality control system based on the concept of sustainable development
and Industry 4.0 will allow a number of benefits to be achieved in the socio-economic area.
The key internal benefits of a manufacturing company include high quality standards;
minimising the risk of errors, complaints, returns, and financial losses; better management
of resources including electricity; early detection and elimination of defects; rapid response;
continuous improvement; and a friendly working environment. On the other hand, external
benefits related to the company’s environment include improving the company’s reputation
in the market, increasing customer satisfaction, increasing the competitiveness of the
company and products, increasing customer loyalty, reducing the negative impact on the
environment, and promoting the concept of sustainable development and Industry 4.0.
2.2. A Detailed Description of an Effective Quality Control System
Based on the assumptions of the concept of an effective quality control system, a
flow chart was created to represent the process of application and maintenance of the
model in any company producing aluminium alloy products. The presented model of
an effective quality control system for aluminium castings concerns the improvement
and optimisation of non-destructive testing methods (X-ray method, ultrasound method„
penetration method, and eddy current detection). These tests make it possible to assess
material properties, recognise material inconsistencies, and measure the dimensions of
objects without altering the functional properties of the tested object [
108
]. The implication
of detection by NDT methods is necessary in the foundry industry to ensure the appropriate
level of quality of manufactured parts. Taking into account the uncertainty of detection
occurring with NDT methods and the specific technological capabilities of each detection
method, the use of two or more successive methods is proposed [
109
]. The selection of
appropriate inspection methods is usually associated with the analysis of a large number of

Sustainability 2025,17, 1418 12 of 34
variables in order to achieve the expected level of quality and organisational and financial
benefits [
110
]. With the use of multiple NDT methods, each successive NDT implication
creates a separate and independent inspection point. As manufacturing companies develop
according to the idea of Industry 4.0, there is an increasing demand for robots used to
perform NDT. This trend is a reflection of a global market shaped by robotics, artificial in-
telligence, high-tech technologies, unmanned automation, 3D CT, real-time digital imaging,
and nanomicroscopy [
111
,
112
]. Taking into account the wide range of possibilities for the
detection of aluminium castings using NDT methods, an important issue is the selection of
adequate methods that correspond to the specificity of the analysed product and related
quality problems [113].
A sequential algorithm was used to build the flowchart, in which the start of the next
activity must be preceded by the completion of the previous one. The algorithm considers
three main stages: preparatory activities, analysis of quality control points in the context of
the model’s assumptions (sustainability, Industry 4.0), and maintenance and improvement.
Within the three stages, the key activities of the implementation procedure and their inputs
and outputs are identified (Figure 2).
The various stages implemented within the model of an effective quality control
system for aluminium castings are conditioned by the need to improve and ensure a stable
level of quality, which indicates an economically reasonable set of complementary features
and functions of the product aimed at a specific customer (group of customers).
•Phase 1. Clarify quality control points within a specific production process
The input element for the first step should be a set of historical data on the imple-
mentation of quality control within the company’s production processes. The selection of
the production process within which the quality control points will be analysed should be
guided by two considerations: the process should lead to the production of an aluminium
alloy product, and the control points located within the process should be primarily imple-
mented using automatic or semiautomatic detection by non-destructive testing.
•Phase 2. Clarify the purpose of the study and select members of the working team
The research objective should be formulated and concretised in the conceptual phase
of the research process and taken into account and controlled in the implementation phase
of the process. At the same time, this process should be a cyclically repetitive sequence of
activities, the beginning and end of which is the state of knowledge (theory and practice)
held and used by the audience. The detailed characterisation of the research objective re-
quires defining the content of the objective (what are we aiming for), its measurement (how
to measure it), the timing of implementation (when to finish it), and the interconnectedness
(how to order the objectives).
The purpose of implementing the developed model using indicator analysis should be
to rank the effectiveness of the total quality control points (detection methods), which will
support decision-making processes in specifying the right development and improvement
projects. The change strategy should take into account the rationale of the concept of
sustainable development and development towards Industry 4.0.
The appointment of members to the expert team is a necessary step for the correct and
effective implementation of the model. Individual team members should have experience
and extensive knowledge of the production implemented in the company and quality
control implemented using NDT.

Sustainability 2025,17, 1418 13 of 34
START
Input
Output
The need to improve qualitv
control of aluminum castings
Selected research
subject
Major qualrtv and
environmental
problems
The need to
improve quality
quality control of
aluminium castings
Understandable
purpose of research,
list of working team
members
Quality control
procedures
technological
documentation
Charakteristics of
quality control
points
Phase 4. Indicator analysis of
partial and total efficiency
levels of the quality control
points
Characteristics
of quality control
points
Specified level of
efficiency of
quality control
points
Phase 2. Clarify the purpose of
the study and select members
of the working team
4.1. Effectiveness of
qualify control points
4.2. Reliability efficiency of
qualify control points
4.3. Energy efficiency of
qualify control points
4.7. Total efficiency
of quality control
points
4.6. Cost
effectiveness of
quality control
points
4.5. Time efficiency
of quality control
points
4.4. Emission
efficiency of quality
control points
A certain level of
efficiency of quality
control points
A range of
efficiencies within
the key features of
quality control
points
A range of
efficiencies within
the key features of
quality control
points
List of
optimisation
measures
Phase 6. Identify improvement
activities to imply and
maintain an effective quality
control system
STOP
Conducting activities
post-optimisation
Analysis of qualitycontrol points in the context of model
assumptions (sustainability, Industry 4.0)
Preparatory activities
Phase 1. Clanfication of quality
control points within a specific
production process
Phase 3. Gathering data on the
quality control points of a
specific production process
Phase 5. Gradation of qualify
control points
Figure 2. Block diagram of an effective quality control system for aluminium castings.
•Phase 3. Gather data on the quality control points of a specific production process
According to the assumptions of the developed model, the detection points where
NDT tests are performed should be studied. The analysis should begin with the collection
of the following data on detection methods: frequency of detection method within the
production process, frequency of identification of nonconformities within the production
process, failures of detection machines and equipment, energy intensity of unit detection,
emissivity of unit detection (chemicals, waste, and pollutants), time of unit detection, and
cost of unit detection. On the basis of historical data, product nonconformities should be
analysed to show the critical defects detected by a specific detection method within the
production process.

Sustainability 2025,17, 1418 14 of 34
•Phase 4. Analysis of the level of partial and total efficiency of quality control points
Analysis of the level of total efficiency of individual quality control points requires
a multifaceted approach related to the determination of partial efficiencies related to the
key characteristics that should be monitored in an effective and efficient quality control
system. Steps 4.1. through 4.5. together with formulas for the individual (sub)efficiencies
are presented in Table 3.
Table 3.
Characteristics of the method of analysis of the level of partial efficiency and total quality
control points.
Step Formula Explanation
4.1. Effectiveness of
quality control points S=CN·(1−F)(1)
where S—checkpoint efficiency [%]; CN—frequency of
detection of a specific type of nonconformity [%]; F—frequency
of occurrence of a specific detection method [%].
4.2. Reliability
efficiency of quality
control points
EP =S·(1−P)(2)
where EP—reliability efficiency of a single detection within a
checkpoint [%]; S—checkpoint efficiency [%]; P—reliability of
detection machinery and equipment within a checkpoint [%].
4.3. Energy efficiency of
quality control points EE =S·(1−EN)(3)
where EE—energy efficiency of a single detection within a
checkpoint [%]; S—checkpoint efficiency [%]; EN—energy
intensity of a single detection within a checkpoint [%].
4.4. Emission efficiency
of quality control points
EM =S·(1−M)(4)
where EM—emission efficiency of a single detection within a
checkpoint [%]; S—checkpoint efficiency [%]; M—emission of
a single detection within a checkpoint [%].
4.5. Time efficiency of
quality control points EC =S·(1−C)(5)
where EC—time efficiency of a single detection within a
checkpoint [%]; S—checkpoint efficiency [%]; C—time of a
single detection within a checkpoint [%].
4.6. Cost effectiveness
of quality control points
EK =S·(1−K)(6)
where EK—cost effectiveness of a single detection within a
checkpoint [%]; S—checkpoint efficiency [%]; K—cost of a
single detection within a checkpoint [%].
4.7. Total efficiency of
quality control points E=S·P·EN·M·C·K(7)
where E—total efficiency of a single detection within a
checkpoint; S—efficiency of a checkpoint; N—energy
intensity of a single detection within a checkpoint;
M—emission of a single detection within a checkpoint;
K—cost of a single detection within a checkpoint.
Determining both partial and total efficiencies allows for detailed analysis and sup-
ports optimisation efforts.
•Phase 5. Gradation of quality control points
Determining the gradation of quality control points involves categorising them accord-
ing to the value of their ranks from the best to the worst and thus from the most effective,
within a process, to the least effective. This step involves comparing the importance of
various quality control points from the organisation’s point of view. The model assumes
that the control points analysed are of equivalent importance. The rank number (ranking)
indicates the importance of a given control point.
In order to implement a multifaceted analysis, it is necessary to create a ranking of the
effectiveness of the total quality control points of the NDT methods and a ranking of the
components of the said indicator.
•
Phase 6. Identify improvement activities to imply and maintain an effective quality
control system

Sustainability 2025,17, 1418 15 of 34
The planning of improvement activities should be a systematically repeated process
and closely related to the results obtained in the form of the main ranking (total efficiency)
and sub-ranking, as well as the identified critical nonconformities in the process. In the
implementation of the improvement model, the marginal benefit rule was taken into
account, according to which retaining only highly effective test methods can lead to a
reduction in the overall detection rate of compliant products and thus in product quality.
Therefore, the proposed improvement actions do not refer to the elimination of the least
effective quality control method but to increasing the benefits of its use and increasing its
effectiveness. According to the model, the improvement activities should be in line with
the concept of sustainable development, so they should address the social, economic, and
environmental dimensions, and at the same time, they should also lead to the implication
of automation and digital solutions directing the company towards Industry 4.0. After
implementing the planned optimisation solutions, the company should start using them,
but the improvement activities should not end. The improved processes should be re-
examined after some time to evaluate the effectiveness of the changes made and plan for
possible adjustments.
Improvement should be seen as a process of continually searching for solutions
aimed at the pursuit of optimisation, which, in terms of the activities of manufacturing
companies, should be equated with achieving the results of their activities beyond the
requirements and expectations placed on them. Through the use of key indicators that
use differentiated data related to control points, it becomes possible to perform rational
and adequate development work. The presented treatment of the model encourages
development activities closely related to the concept of sustainable development through
optimisations in the deployment of checkpoints and the implementation of quality control
itself. The model prompts the implementation of improvement activities that introduce
advanced digital technologies combined with the automation of NDT testing to transform
processes into more autonomous and efficient ones.
3. Model Verification and Results
The verification of the assumptions of the model of an effective quality control system
closely related to the concept of sustainable development and the idea of Industry 4.0
was carried out through its implementation in a selected casting company. The produc-
tion enterprise is located in the southeast part of Poland, and the pieces produced in it
go to the mechanical, automotive, aerospace, medical, and railroad industries. A test
of the model was made by applying it in one of the main production processes in the
company. Production data from the first and second quarters of 2023 were used to verify
the developed model.
•Phase 1. Clarify quality control points within a specific production process
The test of the model was carried out by application to one of the manufacturing
processes producing the leading industrial products in the company. Since the developed
model is closely related to ensuring the high quality of products by increasing the effective-
ness of control points, reducing the level of environmental load, and introducing advanced
digital technologies combined with the automation of NDT testing, the verification of the
model was carried out against the production process of the transfer gear case. In this
process, there were special reasons for the loss of stability: structural changes were made to
the product, and machine relocation and maintenance work were performed, leading to
inconsistent quality of the final products. The manufacturing process, due to the complexity
of technological operations and their level of complexity, uses a number of NDT methods
in inter-operational control. The complex geometry of the final product determines the

Sustainability 2025,17, 1418 16 of 34
need to take into account a variety of control methods at different stages of the production
process. An overview drawing of a transfer gear case casting is shown in Figure 3.
Sustainability 2025, 17, x FOR PEER REVIEW 17 of 36
Figure 3. Overview drawing of transfer gear case.
The transfer gear case with dimensions of 900 × 400 × 250 weighs 80 kg. The product
is made by gravity from AlSi10Mg alloy. The alloy has been widely used in industry due
to its excellent mechanical properties, lightness, corrosion resistance, and high strength-
to-weight ratio. Lightness combined with strength makes the alloy suitable for weight-
sensitive mobile applications in the aerospace, automotive, and medical sectors.
The transfer gear case is a part used to transfer the mace from the engine to the two
drive axles. It is used in off-road vehicles and other all-wheel-drive vehicles.
• Phase 2. Clarify the purpose of the study and select members of the working team
The purpose of the research was to perform an analysis of the control points located
within one of the main production processes, together with the development of improve-
ment measures in the idea of ensuring an effective quality control system. The analysis of
individual quality control points was carried out using key indicators that take into ac-
count the most relevant features of the detection methods used in the context of the idea
of sustainable development supported by advanced digital technologies combined with
automation.
In this step, expert team members were appointed among the company’s manage-
ment staff. The group of experts included a quality control manager, a chief technologist,
a production line manager, an NDT specialist, and a repair and complaints specialist. As
a result, the group of people is characterised by extensive knowledge of the implementa-
tion of the selected manufacturing process (transfer gear case), quality problems of the
product (critical non-conformities), quality control points located within the process, and
the level of complaints about the product.
• Phase 3. Gather data on the quality control points of a specific production process
Non-destructive testing methods located at control points of the manufacturing pro-
cess should be characterised and analysed. These tests include X-ray, ultrasonic, pene-
trant, and eddy current testing. Table 4 synthesises the methods used at checkpoints.
Table 4. Characteristics of non-invasive methods used in quality control points.
No Method Advantages of the Method Disadvantages of the Method Detected Defects
1. X-ray exami-
nation (RTG)
Ability to detect defects in the
entire volume of the tested ob-
j
ect, applicability for any type of
material, ability to test objects
with complex shapes, ability to
accurately determine the size of
discontinuities, non-contact
method.
Detectability of defects depending on
their location relative to the direction
of radiation, high cost of apparatus,
complicated examination process, la-
b
our-intensive process, need to access
two opposing surfaces, radiation haz-
ard.
Spatial discontinuities,
planar discontinuities,
b
listers, residual shrinkage
cavities, shrinkage cracks,
inclusions, detection and
evaluation of thickness
changes in objects and
coatings.
2. Ultrasonic
testing (UT)
Ability to detect defects in the
entire volume of the object to be
Complicated examination process, de-
tection of defects depending on their
Longitudinal and trans-
verse cracks, shrinkage
Figure 3. Overview drawing of transfer gear case.
The transfer gear case with dimensions of 900
×
400
×
250 weighs 80 kg. The product is
made by gravity from AlSi10Mg alloy. The alloy has been widely used in industry due to its
excellent mechanical properties, lightness, corrosion resistance, and high strength-to-weight
ratio. Lightness combined with strength makes the alloy suitable for weight-sensitive
mobile applications in the aerospace, automotive, and medical sectors.
The transfer gear case is a part used to transfer the mace from the engine to the two
drive axles. It is used in off-road vehicles and other all-wheel-drive vehicles.
•Phase 2. Clarify the purpose of the study and select members of the working team
The purpose of the research was to perform an analysis of the control points located
within one of the main production processes, together with the development of improve-
ment measures in the idea of ensuring an effective quality control system. The analysis
of individual quality control points was carried out using key indicators that take into
account the most relevant features of the detection methods used in the context of the idea
of sustainable development supported by advanced digital technologies combined with
automation.
In this step, expert team members were appointed among the company’s management
staff. The group of experts included a quality control manager, a chief technologist, a
production line manager, an NDT specialist, and a repair and complaints specialist. As a
result, the group of people is characterised by extensive knowledge of the implementation
of the selected manufacturing process (transfer gear case), quality problems of the product
(critical non-conformities), quality control points located within the process, and the level
of complaints about the product.
•Phase 3. Gather data on the quality control points of a specific production process
Non-destructive testing methods located at control points of the manufacturing pro-
cess should be characterised and analysed. These tests include X-ray, ultrasonic, penetrant,
and eddy current testing. Table 4synthesises the methods used at checkpoints.
The RTG examination carried out as part of the model verification was performed us-
ing a BOSELLO X-ray chamber (Carl Zeiss X-Ray Technologies Srl, Cassano Magnago, Italy),
which uses non-destructive testing technology. The device allows for volumetric examina-
tions with (real-time) digital imaging. By using an X-ray system without photographic film,
it is possible to view the image immediately, eliminating the cost of photographic materials
and facilitating easy archiving and data exchange.

Sustainability 2025,17, 1418 17 of 34
Table 4. Characteristics of non-invasive methods used in quality control points.
No Method Advantages of the Method Disadvantages of the Method Detected Defects
1.
X-ray
examination
(RTG)
Ability to detect defects in the
entire volume of the tested
object, applicability for any
type of material, ability to test
objects with complex shapes,
ability to accurately determine
the size of discontinuities,
non-contact method.
Detectability of defects
depending on their location
relative to the direction of
radiation, high cost of
apparatus, complicated
examination process,
labour-intensive process, need
to access two opposing
surfaces, radiation hazard.
Spatial discontinuities,
planar discontinuities,
blisters, residual shrinkage
cavities, shrinkage cracks,
inclusions, detection and
evaluation of thickness
changes in objects
and coatings.
2. Ultrasonic
testing (UT)
Ability to detect defects in the
entire volume of the object to
be tested, high sensitivity of
the test, ability to test entire
components simultaneously or
locally, ability to accurately
locate and size discontinuities,
ability to measure the thickness
of the object.
Complicated examination
process, detection of defects
depending on their location
relative to the direction of
ultrasonic waves, the need for
expensive equipment.
Longitudinal and
transverse cracks, shrinkage
cavities, blisters, inclusions,
delamination, sticking,
fusion deficiencies, welding
undermelting,
corrosion cavities.
3. Penetration
testing (PT)
No need for expensive testing
equipment, ability to test
objects of complex shape.
Detection of surface defects
only, relatively long testing
time, higher cost of testing, the
need for thorough preparation
of the surface to be tested, the
large influence of the
roughness of the surface to be
tested on the test result, the
harmfulness of the
preparations used for the
health and environment.
Hot cracks, cold cracks,
fatigue wear cracks,
sticking, fusion deficiencies
in welded joints, hardening
cracks, rolling and cupping,
as well as corrosion and
erosion pitting.
4.
Eddy
current
testing (ET)
Very high reliability and
sensitivity of testing, high
speed of interpretation of
results, no need to remove
protective coatings, no need for
a coupling agent (probe
surface), largely non-contact
testing, no limitations due to
poor weather or lighting
conditions such as during
penetrant testing, no
radiological hazards.
Ability to use it only for the
examination of conductive
objects, significant influence of
the surface roughness of the
test item, the depth of
penetration is a maximum of
several millimetres, defects
located parallel to the
windings of the probe coil, the
direction of scanning may
remain undetected, the
sensitivity of detection of
discontinuities decreases as the
depth of penetration increases.
Surface discontinuities
(cracks) and subsurface (to
a depth of a few
millimetres), dimensional
measurements, electrical
conductivity.
Industrial UT detection was carried out using a device manufactured by Olympus
(Hamburg, Germany). The survey is fully digital and controlled by a microprocessor.
PT testing was carried out on a handheld bench using a set of industrial chemistry
tools: developer, remover, Pfinder brand penetrant, and a handheld LED lamp from the
KingKing series using LED technology.
ET testing was carried out using a specialist defectoscope. The head of the device is
applied to the area under test, via a KUKA robotic arm, and the test probe receives the

Sustainability 2025,17, 1418 18 of 34
return response of the electromagnetic field. The test result is presented graphically on the
display of the current defectoscope.
The assumptions of the developed model indicate that within the characterisation of
NDT methods used in quality control, it is also necessary to determine their most important
features from the company’s point of view: frequency of detection method within the
production process, frequency of identification of nonconformities within the production
process, failures of detection machines and equipment, energy intensity of unit detection,
emissivity of unit detection (chemicals, waste, and pollutants), time of unit detection, and
cost of unit detection (Table 3). The frequency of a detection method indicates how often
a specific detection method is used within the control system of a specific product. The
frequency parameter indicates how often defects are detected in a product using a specific
detection method. Failures of machines and detection equipment indicates the level of
failure of equipment used for inspection with a specific method. The energy intensity
informs about the level of electricity consumption during the detection of a single piece of
product by a specific NDT method. The emissivity of a unit detection is a parameter that
tells about the ability of a given detection method to emit chemicals, waste, and pollutants
during a unit detection. The values of the indicators of detection unit time and detection
unit cost inform about the time of realisation of a single test and their costs. The detection
methods indicated in Table 3are carried out within the quality control system of a specific
product, and therefore, their values within individual characteristics are equal to 100%. The
values in percentage terms illustrate the distribution of the magnitude of the individual
parameters within a specific quality control system using the following detection methods:
RTG, UT, PT, and ET.
Implementing a multifaceted checkpoint analysis also requires the identification of
critical product nonconformities (Table 5).
Table 5. Characteristics of key features of quality control methods.
Checkpoint Feature RTG UT PT ET
Frequency of detection method [F] 16.67% 16.67% 33.33% 33.33%
Frequency of non-compliance identification [CN]
39.23% 16.41% 28.17% 16.19%
Machine and detection equipment [P] 25.72% 19.67% 21.12% 33.49%
Energy intensity of unit detection [EN] 41.04% 7.15% 34.06% 18.75%
Emissivity of unit detection [M] 53.11% 22.36% 10.16% 14.37%
Unit detection time [C] 12.29% 32.18% 35.55% 19.98%
Cost per unit detection [K] 43.35% 8.87% 37.01% 10.77%
The most frequently identified nonconformities
The presence of
oxides and
shrinkage cavities
Lint, inclusions,
lack of remelting Hot cracks Cracks
Production data from the first and second quarters of 2023 were used to identify
critical casting non-conformities. The data show that the critical non-conformities were the
presence of oxides, shrinkage cavities, rows, and internal cracks. These nonconformities
were detected by examination with RTG, UT, PT and ET. Cracks were detected by PT and
ET testing, but these tests were performed against other areas of the casting. The critical
incompatibilities detected by each detection method affect the performance parameter.
The data collected in phase 3 provide the necessary set of information to implement
a multifaceted analysis of the effectiveness of quality control points located within the
transfer gear case production process.
•Phase 4. Analysis of the level of partial and total efficiency of quality control points

Sustainability 2025,17, 1418 19 of 34
The results of the study of the level of partial and total efficiency of automatic or
semi-automatic detection methods are shown in Table 6.
Table 6. The level of partial and total efficiency of checkpoints.
Method
Checkpoint Feature
S EP EE EM EC EK E
RTG 32.69% 24.28% 19.27% 15.33% 36.71% 18.52% 0.01594%
UT 13.67% 10.98% 12.70% 10.62% 9.27% 12.46% 0.00023%
PT 18.78% 14.81% 12.38% 16.87% 12.10% 11.83% 0.00083%
ET 10.79% 7.18% 8.77% 9.24% 8.64% 9.63% 0.00005%
As indicated in Table 6, the RTG test showed the highest level of effectiveness. Quality
control using this method was carried out once within the entire transfer gear case pro-
duction process and was located after the product casting process. The checkpoint has
vital importance for ensuring the appropriate level of product quality. The whole casting is
subject to detection. At this point, two types of non-conformities considered critical were
identified: the presence of oxides and shrinkage cavities. In terms of the effectiveness of
the other checkpoints, the PT, UT, and ET methods scored 18.78%, 13.67%, and 10.79%,
respectively. The PT method occurred twice in the production process, and the sum of the
frequency of identifying non-conformities was less than 30%, which contributed to the
efficiency result of 18.78%. The UT method and the ET method showed almost identical
non-conformity identification frequency parameters, hence the similar efficiency values of
the two methods.
Efficiency in terms of reliability of quality control points takes into account the level
of failure of machines and detection equipment. The highest level of this efficiency was
characterized by the RTG method (Table 5), which indicated a high level of correctness of
operation and fulfilment of all assigned functions and activities during the period it was
supposed to work and under the specified operating conditions. The significant difference
between the parameter studied by the RTG method and the other quality control methods
indicates the need to work on regulatory, diagnostic, and maintenance activities in order
to provide an adequate level of reliability and safety during use. It can be expected that
the result of this rational activity will be an optimal system for the operation of facilities
according to their technical condition (cheap, effective, and efficient), guaranteeing correct
adjustment, proper technical condition, and the required state of reliability.
The energy efficiency level of the quality control point in which the RTG method was
used was the highest (Table 5). Despite the high energy consumption for the test unit
and the impact of the other relevant efficiency parameters (frequency of detection method
and frequency of identification of nonconformities), the energy efficiency of this method
showed a more than 6.5% advantage over the UT and PT methods and 10.67% over the
ET method. The identified relationship confirms the reasonableness of the costs associated
with the implementation of RTG testing.
The efficiency of the emission of quality control points is an important aspect in terms
of reducing negative environmental impacts during process execution. The highest level of
this efficiency is characterized by PT testing (Table 5), which is due to the high specificity
of this method. With PT testing, it is important to mention the risks associated with the
use of test preparations. Exposure to penetrants, removers, and developers can cause
irritation of the respiratory tract, skin, and mucous membranes, so operators should wear
personal protective equipment, and the rooms where the tests are performed should have
sufficient ventilation. These agents are also flammable and harmful to the environment, so
regulations related to the use of such materials should be observed. RTG examination also

Sustainability 2025,17, 1418 20 of 34
shows a significant emission efficiency. Due to the presence of radiation, this method is
dangerous to operators and bystanders, so it is essential to comply with regulations related
to radiation protection and equipment storage.
The time efficiency of the quality control points refers to the time consumption of the
examination per unit inspected. Due to the labour-intensive and time-consuming nature of
the implementation of the RTG method (Table 5), the method achieved the highest time
efficiency parameter. The significantly higher level of the parameter is also influenced
by the high level of efficiency of the method. Second, the PT method had a significantly
lower level of time efficiency. The significant time efficiency of the test is due to the need
to prepare the test surface, apply penetrants, wait for penetrant penetration, and apply
developer. The time efficiency of UT and ET testing was almost the same; these tests are
carried out in a relatively short time.
Cost effectiveness is another important aspect of shaping an effective and efficient
quality control system. According to Table 5, the RTG method is a relatively expensive
method due to the need for complex apparatus and consumables, which, combined with
the high level of efficiency of the method, generated a parameter value of 18.52%. Detection
with examination of the UT method had the lowest cost, but due to the significant level of
efficiency of the method, it reached a value 2.57% higher than the ET method.
Figure 4summarises the sub-efficiency results achieved by the detection methods
used within the production process analysed.
Sustainability 2025, 17, x FOR PEER REVIEW 21 of 36
Detection with examination of the UT method had the lowest cost, but due to the signifi-
cant level of efficiency of the method, it reached a value 2.57% higher than the ET method.
Figure 4 summarises the sub-efficiency results achieved by the detection methods
used within the production process analysed.
A comparison of the partial efficiencies of the detection methods tested (Figure 4)
shows that, among the individual detection methods, the RTG method had the greatest
variation in partial efficiencies. The RTG method achieved the highest level of efficiency
within the duration of a single test, which was related to the automation of detection. The
UT, PT, and ET methods showed less variation in efficiency levels than the RTG method.
The UT method had the highest level of efficiency within the cost of performing quality
control and the lowest within the cost of detection. The PT method had the highest effi-
ciency within the emissivity and the lowest within the cost of testing. The ET method
achieved the highest efficiency within the cost of testing and the lowest within the relia-
bility of a single detection within a checkpoint.
Figure 4. Summary of the level of partial efficiencies of the tested detection methods.
Based on the partial efficiencies of the individual checkpoints, their total efficiencies
were calculated, as indicated in Table 6. Within the framework of the production process
studied and the quality control points located within it, detection by the RTG method
significantly exceeded the values obtained by the other methods. The total efficiency of
the RTG method was 0.01594%. The high value of the analysed parameter indicates the
importance of the checkpoint in ensuring the expected quality level of the transfer gear
case casting and reducing production costs. The ET test reached the lowest parameter
(0.00005%), which is due to the fact that this method within each of the partial parameters
reached the lowest values compared to the other methods analysed.
• Phase 5. Gradation of quality control points
Phase 5 ranked the quality control methods based on the parameter of effectiveness,
partial effectiveness, and total effectiveness. Within the rankings, the quality control meth-
ods were placed from the best to the worst, that is, from the most effective, within the
process, to the least effective from the company’s point of view. The result is shown in
Table 7.
Table 7. Gradation of the partial and total effectiveness of quality control methods.
Figure 4. Summary of the level of partial efficiencies of the tested detection methods.
A comparison of the partial efficiencies of the detection methods tested (Figure 4)
shows that, among the individual detection methods, the RTG method had the greatest
variation in partial efficiencies. The RTG method achieved the highest level of efficiency
within the duration of a single test, which was related to the automation of detection. The
UT, PT, and ET methods showed less variation in efficiency levels than the RTG method.
The UT method had the highest level of efficiency within the cost of performing quality
control and the lowest within the cost of detection. The PT method had the highest efficiency
within the emissivity and the lowest within the cost of testing. The ET method achieved
the highest efficiency within the cost of testing and the lowest within the reliability of a
single detection within a checkpoint.

Sustainability 2025,17, 1418 21 of 34
Based on the partial efficiencies of the individual checkpoints, their total efficiencies
were calculated, as indicated in Table 6. Within the framework of the production process
studied and the quality control points located within it, detection by the RTG method
significantly exceeded the values obtained by the other methods. The total efficiency of
the RTG method was 0.01594%. The high value of the analysed parameter indicates the
importance of the checkpoint in ensuring the expected quality level of the transfer gear
case casting and reducing production costs. The ET test reached the lowest parameter
(0.00005%), which is due to the fact that this method within each of the partial parameters
reached the lowest values compared to the other methods analysed.
•Phase 5. Gradation of quality control points
Phase 5 ranked the quality control methods based on the parameter of effectiveness,
partial effectiveness, and total effectiveness. Within the rankings, the quality control
methods were placed from the best to the worst, that is, from the most effective, within
the process, to the least effective from the company’s point of view. The result is shown
in Table 7.
Table 7. Gradation of the partial and total effectiveness of quality control methods.
The Analysed Feature of the Checkpoint Checkpoint Ranking
Effectiveness of quality control points RTG > PT > UT > ET (8)
Reliability efficiency of quality control points RTG > PT > UT > ET (9)
Energy efficiency of quality control points RTG > UT > PT > ET (10)
Emission efficiency of quality control points RTG > PT > UT > ET (11)
Time efficiency of quality control points RTG > PT > UT > ET (12)
Cost effectiveness of quality control points RTG > UT > PT > ET (13)
Total efficiency of quality control points RTG > PT > UT > ET (14)
The highest level of efficiency of the quality control points, according to the charac-
teristics studied, was characterised by RTG examination (Table 6). This means that this
checkpoint is important to ensure the quality level of transfer gear case castings. The strong
ranking and therefore significant effectiveness of the method is further confirmed by the
fact that two types of critical nonconformities (the presence of oxides and shrinkage cavities
in the casting) were identified using RTG examination. This checkpoint is the first link in
the quality control chain within the production process.
Within both the efficiency index and each type of efficiency, the ET test was at the end
of the ranking (Table 6). This survey has a specific role in the quality control of the transfer
gear case casting; as part of the production process, the survey was performed against the
axially symmetrical holes of the casting. Due to the significant economics of the test and
the load on the mentioned areas, this detection method was considered optimal. Partial
efficiencies were influenced by the parameters of the features (Table 4), which, because the
test was implemented at two control points, showed significantly low values; in the case of
the analysis of single control points by the ET method, the values of the features (Table 4)
should be divided in half.
In terms of energy and cost efficiency, the same rankings were made ((10) and (13)),
according to which the RTG study was the most efficient, followed by the UT, PT, and
ET studies. In both cases, the differences in parameter between the UT and PT tests were
relatively small (energy efficiency: 0.31%; cost efficiency: 0.63%).
The following order of RTG, PT, UT, and ET detection methods was observed in
the predominant ranking (effectiveness, effectiveness in terms of reliability, emissions,
and time) and in the ranking of total effectiveness (order of tests starting from the most
effective one). The indicated result was significantly influenced by differences in the value

Sustainability 2025,17, 1418 22 of 34
of the efficiency parameter, which is one of the priorities of an effective quality control
system. Furthermore, the dependencies obtained from the NDT tests were influenced by
the specificity of each method, which was highlighted by the repeatability of five of the
seven classifications.
The results of the gradation of the effectiveness of the partial quality control methods
discussed are summarised using a line graph (Figure 5).
Sustainability 2025, 17, x FOR PEER REVIEW 23 of 36
Figure 5. Gradation structure of the analysed parameters of the tested detection methods.
The gradation structure of the parameters analysed (Figure 5) clearly illustrates the
level of efficiency of the individual control points. The most efficient method within the
production process was the RTG method, while the method with the lowest efficiency
level was the ET method.
The inclusion of partial efficiencies and their rankings in the analysis along with the
ranking of total efficiencies makes it possible to perform a multifaceted interpretation of
the key features of the checkpoints. Such an approach to the problem makes it possible to
consciously and prudently shape an effective quality control system.
• Phase 6. Identify improvement activities to apply and maintain an effective quality
control system
To develop adequate shaping-improvement activities, the first consideration was
given to identifying development needs from the perspective of the results obtained in
phases 4 and 5 as well as the strategy, objectives, area of responsibility, and changes at the
level of knowledge, skills, and aitudes of employees. When defining future activities,
aention was also paid to the main rationale of the developed model—taking care of the
environmental, economic, and social aspects and taking into account the implications of
automation and digital solutions directing the company towards Industry 4.0. The im-
provement activities concern the relocation of control points, the change in the type of
detection implemented, and the increase in automation and integration of machines into
company systems.
Due to the high level of effectiveness and total efficiency, it was decided not to make
modifications to the RTG examination. This examination, although energy intensive, time-
consuming and significantly expensive, brings many benefits to the company: the highest
(among the tested methods) value of the indicator of “frequency of identification of non-
conformities” and the identification of two types of nonconformities considered critical:
the presence of oxides and shrinkage cavities. However, with regard to the RTG examina-
tion, it was observed that there is a need to undertake work in the field of regulatory,
diagnostic, and maintenance activities to ensure their adequate level of reliability and
safety during their use.
Regarding PT detection, it is possible to reduce the parameter “unit detection time”,
which is currently the highest among the methods analysed.
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
Checkpoint
efficiency [S]
Reliability
efficiency of
quality control
points [EP]
Energy
efficiency of
quality control
points [EE]
Emission
efficiency of
quality control
points [EM]
Time
efficiency of
quality control
points [EC]
Cost
effectiveness
of quality
control points
[EK]
RTG 32.69% 24.28% 19.27% 15.33% 36.71% 18.52%
UT 13.67% 10.98% 12.70% 10.62% 9.27% 12.46%
PT 18.78% 14.81% 12.38% 16.87% 12.10% 11.83%
ET 10.79% 7.18% 8.77% 9.24% 8.64% 9.63%
Percentage of characteristics analysed
Figure 5. Gradation structure of the analysed parameters of the tested detection methods.
The gradation structure of the parameters analysed (Figure 5) clearly illustrates the
level of efficiency of the individual control points. The most efficient method within the
production process was the RTG method, while the method with the lowest efficiency level
was the ET method.
The inclusion of partial efficiencies and their rankings in the analysis along with the
ranking of total efficiencies makes it possible to perform a multifaceted interpretation of
the key features of the checkpoints. Such an approach to the problem makes it possible to
consciously and prudently shape an effective quality control system.
•
Phase 6. Identify improvement activities to apply and maintain an effective quality
control system
To develop adequate shaping-improvement activities, the first consideration was
given to identifying development needs from the perspective of the results obtained in
phases 4 and 5 as well as the strategy, objectives, area of responsibility, and changes at the
level of knowledge, skills, and attitudes of employees. When defining future activities,
attention was also paid to the main rationale of the developed model—taking care of the
environmental, economic, and social aspects and taking into account the implications
of automation and digital solutions directing the company towards Industry 4.0. The
improvement activities concern the relocation of control points, the change in the type of
detection implemented, and the increase in automation and integration of machines into
company systems.
Due to the high level of effectiveness and total efficiency, it was decided not to make
modifications to the RTG examination. This examination, although energy intensive,
time-consuming and significantly expensive, brings many benefits to the company: the
highest (among the tested methods) value of the indicator of “frequency of identification
of nonconformities” and the identification of two types of nonconformities considered

Sustainability 2025,17, 1418 23 of 34
critical: the presence of oxides and shrinkage cavities. However, with regard to the RTG
examination, it was observed that there is a need to undertake work in the field of regulatory,
diagnostic, and maintenance activities to ensure their adequate level of reliability and safety
during their use.
Regarding PT detection, it is possible to reduce the parameter “unit detection time”,
which is currently the highest among the methods analysed.
The value of the partial efficiencies of the ET method and the total efficiency is a
consequence of the relatively low values of the input characteristics. The value of the
parameter “frequency of identification of nonconformities” is the lowest among those
analysed, having a great impact on the level of efficiency, which ultimately significantly
determines other calculated efficiencies. With a low level of efficiency, it is worth noting
that this method is used to identify critical non-conformities. ET testing plays a specific role
in the quality control of the transfer gear case casting; as part of the production process, the
test is carried out against the axially symmetrical holes of the casting. Due to the significant
economics of the test and the operational load of the mentioned areas, this detection method
was considered optimal. In order to increase the efficiency of the testing by the ET method,
relocation should be carried out. The first inspection points should be carried out after the
surface-roughing operation and the second inspection point before the chemical coating
process. The indicated measures will allow for a relatively early identification of cracks,
which are the most dangerous non-conformities in the context of product operation. An
additional measure for the ET method should be the transition from semiautomatic to
automatic inspection, which will help reduce detection time and failure rates. Along with
automation, digital solutions that integrate machines with systems in the company should
be applied. Such a solution will allow tracking of the achieved control results in real mode,
enabling quick responses to potential quality deviations in the process.
After analysing the parameters and effectiveness of the partial checkpoints that use
the UT method, it was proposed to change the nature of the ongoing (inter-operational)
inspection carried out without changing its location within the production process. Within
the two checkpoints, the detection should change from 100-percent inspection to random
inspection of castings, the course of which was established on the basis of statistical data
and probability calculus. In addition, digital applications should be considered for the UT
method so that detection data are recorded in real time. With the proposed changes, the
component parameters of all indicators analysed will increase, which will positively affect
the overall efficiency of the UT method.
With reference to the implications of the idea of Industry 4.0, it would make sense
within the framework of improvement activities to implement the so-called IoT (Internet
of Things). This solution will ensure constant communication between the automated
quality control points being analysed (non-destructive testing machines) and the exchange
of information gathered in real time thanks to the numerous sensors that will be installed.
The presented solution will enable a thorough analysis of the entire quality control system
and immediate action to be taken in the event of any irregularities. The development of
this space will be aided by the use of advanced technologies such as cloud computing and
complementary edge computing and big data analysis. In this area, the implementation of a
predictive maintenance approach would also be an important improvement. Implementing
predictive maintenance of machines used for quality control would reduce the costs associ-
ated with their servicing and repair as well as the costs associated with their downtime. The
means to do this include various types of sensors (temperature, vibration, and humidity),
which, networked together, will provide, via an IoT platform, a large amount of data on
the operation of machines.

Sustainability 2025,17, 1418 24 of 34
The values of the individual components that affect the total efficiency index of the
quality control methods analysed will increase due to the implications of the improvement
activities. The proposed development strategy includes activities in the areas of increasing
the level of automation, modernisation of the machinery and equipment park, digitisation,
and relocation of control points. This treatment would significantly affect the cost per
unit test and stabilise the level of product quality (transfer gear case). The proposed
improvement measures are in line with the idea of sustainable development and lead
to the adoption of a manufacturing model called Industry 4.0 as a result of the use of
feedback between automation, processing, and data exchange. The final reduction in
energy intensity is reflected in the price of the final product, and reducing manufacturing
costs while maintaining high quality ensures a stable position in the market. Currently, the
modern foundry industry is increasingly focussing on the problem of the energy intensity
of manufacturing processes and related activities, and reducing the use of energy will allow
the industry to achieve even better economic results and reduce the negative impact on the
environment in the future.
4. Discussion
Nowadays, there is a great dynamic of changes taking place globally, regionally, and
locally. Changes can be seen taking place in many spheres, including the economic, envi-
ronmental, social, political, and legal spheres, in the methods of conducting and developing
business and in the intensification of competition [
2
,
5
]. The progressive development of
technology and engineering, especially information and telecommunications systems, has
outlined new opportunities in the area of production and the improvement of existing
methods and creation of new methods of management. The conditions for the operation
of manufacturing companies within global and internal markets are also undergoing sig-
nificant modifications [
8
,
10
]. Entrepreneurs, in order to be competitive, must monitor and
reliably receive stimuli and signals from the environment, use them, implement innovative
solutions in their products, and improve the functioning of the enterprise by optimising
processes and management systems. The knowledge-based economy is becoming apparent,
the importance of resources at the disposal of the company is changing, and intangible
capital is beginning to dominate among the resources of manufacturing enterprises [
16
,
22
].
The current view of the business ecosystem is a consequence of the change in the
conditions and operating environment of business organisations and the modern manufac-
turing enterprise. It requires understanding and adopting business management strategies
in conjunction with current market conditions geared towards the sustainable development
and transformation of businesses into smart factories (Industry 4.0) [58,59,62].
Literature studies that refer to the implications of the premises of the concept of
sustainable development in manufacturing companies mainly refer to the idea of clean
production [
87
–
89
] while overlooking the closely related process of the quality control of
products, which is carried out as part of these processes. The quality control process has a
direct impact on the quality of the final product. The most important role of quality control
is to prevent losses resulting from defective production [
84
,
85
]. Also, with regard to this
group of activities, efforts should be made related not only to the detection itself but also to
its other features affecting, for example, the environment.
The literature indicates that research is being carried out on improving quality control
in aluminium castings from the early stages of the process—observing proper practices [
91
].
Authors have also focused on the study of molten liquid metal using sensors. The benefits
of online alloy quality monitoring, e.g., faster feedback to improve process control, are
also highlighted [
92
,
93
]. Research is also being conducted on the use of infrared thermog-
raphy to assess the quality of liquid metals [
94
]. Applications are being developed for

Sustainability 2025,17, 1418 25 of 34
measurement of the level of nonferrous molten metal and molten metal flow control [
84
]
as well as proprietary automated methods that use redundant views of the test object to
perform inspection [
98
] and new laser-assisted optical inspection methods [
99
]. Compared
to the developed model, the presented methods do not focus on the entire quality control
process but only on the improvement of a single inspection point. The proposed model
is closely related to the idea of Industry 4.0 and the concept of sustainable development,
while the models presented only refer to premises that indicate the idea of Industry 4.0. In
the literature, there is a lack of studies related to the multifaceted analysis of quality control,
taking into account only the features that refer to the idea of sustainable development. One
can only encounter studies that refer to sustainability-related improvements directed to
particular areas of production companies’ operations or to the production of products that
are more environmentally friendly.
Research carried out in accordance with the presented model is connected with global
trends: conduct in accordance with the concept of sustainable development and the idea of
smart factories. Thus, we discuss ensuring the consistency of three key elements, namely
economic growth, social inclusion, and environmental protection, which is supported by
the implementation of the assumptions of the operation of smart factories (a high degree
of digitisation and the use of new technologies at many positions). The developed model
takes as the object of analysis automated or semi-automated quality control methods,
including the most commonly used NDT methods. Quality control carried out using non-
destructive methods does not affect the continuity of the structure or the service properties
of the material, which is their primary advantage over destructive testing. The use of
non-destructive testing is associated with high efficiency, accuracy, and relatively short
execution time, and it translates directly into increased human safety by improving the
reliability of machine and equipment components, reducing operating costs, increasing
product quality, and extending product life. However, this type of quality control is most
often also energy-, emission-, and cost-intensive. Therefore, it is important to analyse the
set of characteristics presented in the context of the relationships that exist between them.
The quality control within the transfer gear case production process made it possible
to draw up a diagnosis of individual control points in total and partial terms. The analysis
of detection methods from the perspective of stand-alone quality control points made it
possible to distinguish the key characteristics of detection that should be achieved and to
assess the level of fulfilment of these characteristics. In the context of concern for the eco-
nomics of manufacturing enterprises, the environment, and society, the following features
of quality control points for foundry products were distinguished: efficiency, reliability-
related efficiency, energy efficiency, emission efficiency, time efficiency, cost efficiency, and
total efficiency. The presented features of quality control points are inextricably linked with
each other. Figure 6illustrates the level of features achieved by each detection method.
A comparison of the partial efficiencies of the tested quality control methods (Figure 6)
shows that X-ray examination within each feature showed the highest level of efficiency.
This fact underlines the importance of testing in the context of ensuring an adequate level
of quality for the transfer gear case castings. This inspection point represents the first link
in the quality control chain within the production process.
Detection by the ET method achieved the lowest values of the efficiencies analysed.
It should be emphasised that the test has a specific role in quality control of the transfer
gear case casting—detection performed in relation to the axially symmetrical holes of the
casting. Due to the operational load on these areas of the product and the considerable
economics of the test, this detection method was considered optimal.

Sustainability 2025,17, 1418 26 of 34
Sustainability 2025, 17, x FOR PEER REVIEW 26 of 36
of quality control, taking into account only the features that refer to the idea of sustainable
development. One can only encounter studies that refer to sustainability-related improve-
ments directed to particular areas of production companies’ operations or to the produc-
tion of products that are more environmentally friendly.
Research carried out in accordance with the presented model is connected with
global trends: conduct in accordance with the concept of sustainable development and the
idea of smart factories. Thus, we discuss ensuring the consistency of three key elements,
namely economic growth, social inclusion, and environmental protection, which is sup-
ported by the implementation of the assumptions of the operation of smart factories (a
high degree of digitisation and the use of new technologies at many positions). The devel-
oped model takes as the object of analysis automated or semi-automated quality control
methods, including the most commonly used NDT methods. Quality control carried out
using non-destructive methods does not affect the continuity of the structure or the service
properties of the material, which is their primary advantage over destructive testing. The
use of non-destructive testing is associated with high efficiency, accuracy, and relatively
short execution time, and it translates directly into increased human safety by improving
the reliability of machine and equipment components, reducing operating costs, increas-
ing product quality, and extending product life. However, this type of quality control is
most often also energy-, emission-, and cost-intensive. Therefore, it is important to analyse
the set of characteristics presented in the context of the relationships that exist between
them.
The quality control within the transfer gear case production process made it possible
to draw up a diagnosis of individual control points in total and partial terms. The analysis
of detection methods from the perspective of stand-alone quality control points made it
possible to distinguish the key characteristics of detection that should be achieved and to
assess the level of fulfilment of these characteristics. In the context of concern for the eco-
nomics of manufacturing enterprises, the environment, and society, the following features
of quality control points for foundry products were distinguished: efficiency, reliability-
related efficiency, energy efficiency, emission efficiency, time efficiency, cost efficiency,
and total efficiency. The presented features of quality control points are inextricably linked
with each other. Figure 6 illustrates the level of features achieved by each detection
method.
Figure 6. Level of partial efficiencies of the detected detection methods tested.
Figure 6. Level of partial efficiencies of the detected detection methods tested.
By assessing the level of fulfilment of the features, detecting sensitive areas, and on
this basis adopting an adequate development strategy, it is possible to increase the level
of partial efficiencies and then total efficiency. A multifaceted analysis of fundamental
issues makes it possible to make and implement rational optimisations. The determination
of the level of efficiency of individual control points within the production process is the
basis of the process of ensuring a high level of the quality of cast products. The diagnosis
of the level of efficiency of quality control points and proposals for improvement and
optimisation measures are presented in Table 8.
Table 8. Diagnosis of checkpoint effectiveness with improvement measures.
Method
Frequency of Occurrence in the Ranking of
Partial Efficiency Improvement Activities
I
Position
II
Position
III
Position
IV
Position
RTG 6 0 0 0
•
Undertake work on regulatory, diagnostic, and maintenance
activities to ensure an adequate level of reliability and safety
in the implementation of quality control.
PT 0 4 2 0
•Conduct training for employees in the implementation of
testing to reduce the value of the parameter “unit
detection time”.
UT 0 2 4 0
•Change the nature of quality control from 100% to spot
checks of castings (at both quality control points).
•Application of digital solutions at both checkpoints to
integrate the machine with the company’s systems
(real-time tracking of quality control results enabling
rapid response).
ET 0 0 0 6
•Relocation of control points (the first quality control point
should be located after the surface-roughing operation; the
second quality control point should be located before the
chemical coating process) to increase detection efficiency.
•
Shift from semi-automatic to automatic inspection to reduce
detection time and failure rate.
•
Implementing digital solutions to integrate the machine into
the company’s systems (real-time tracking of quality control
results for quick response).

Sustainability 2025,17, 1418 27 of 34
Ranking the effectiveness of total quality control points:
RTG > PT > UT > ET (15)
In terms of effectiveness and partial efficiency, the examination performed with the
RTG method was always located in the first position (Table 8). This indicates the high
level of relevance of this method in terms of identifying nonconformities in castings and,
consequently, ensuring a tight quality management system. The high level of efficiency of
the RTG method is important because this examination was performed at the first quality
control point within the production process, and two types of critical non-conformities (the
presence of oxides and shrinkage cavities in the casting) were identified using this method.
Within the detailed efficiency rankings developed, there are differences between
the ranking locations of the PT and UT methods (Table 8). In terms of four of the six
characteristics considered key to the efficiency checkpoints, the PT methods were found
to be significantly higher than the UT methods (efficiency, reliability efficiency„ emission
efficiency, and time efficiency) (Table 5). However, in the other two cases, the parameters
of the two methods were very similar (energy efficiency: 0.31%; cost efficiency: 0.63%).
The result was influenced by similar values of the parameters that determined the key
characteristics of the detection methods.
Within both the effectiveness index and each type of effectiveness, the ET testing
was at the bottom of the ranking; the method was located in the fourth position of the
ranking (Table 8) six times. This result does not prove the need to remove this method
from the product quality control chain. This test has a specific role in quality control: the
implementation of detection within the axially symmetrical holes of the casting. Due to
the specific geometry of the casting (the operating load of the mentioned areas) and the
considerable economics of the test, this method of detection was considered optimal.
Defining optimisation and improvement activities was based on parameter analysis
of key features of detection methods, which influenced the ranking of partial efficiency of
quality control points in actions that are mainly related to the following:
•
Regulatory, diagnostic, and maintenance activities of the machinery and equipment
used for detection;
•
Employee training activities (periodic and routine training sessions to enhance knowl-
edge and skills);
•A change in the nature of quality control from 100% to spot checks;
•Relocation of quality control points within the production process;
•Automation of quality control points;
•
The application of digital solutions (integrating the quality control machine and
equipment into the company’s systems).
The indicated improvement activities aimed at improving the efficiency of the qual-
ity control system, taking into account the main assumptions of the idea of sustainable
development and Industry 4.0. On the basis of the indicated improvement activities in
relation to the analysed quality control methods, the production enterprise will be able
to ensure the high quality of the supplied products (aluminium castings). However, it
is important to bear in mind that building a tight quality system is time-consuming (the
complexity of implementing improvement activities) and costly (the initial investment,
e.g., due to relocation of control points), and the results may be delayed. In addition,
there may be a number of limitations associated with the automation of detection in real
production conditions. The improvement process requires shaping the mentality and level
of awareness of employees in order to gain a detailed understanding of the quality controls
and processes in place.

Sustainability 2025,17, 1418 28 of 34
The originality and superiority of the presented concept of ensuring an effective quality
control system for aluminium castings is manifested in the multifaceted analysis, which
allows a detailed study of quality control points (and the NDT methods implemented in
them), taking into account not only the detection (or not) of various types of defects but also
other key features. The following were identified as key features of quality control methods
from the point of view of efficient and effective business management: effectiveness,
efficiency in the context of reliability, energy efficiency, emission efficiency, time efficiency,
and cost efficiency. The distinguished features provide a wide and complex range of
information. As a result, the developed model allows for the implementation of control
that ensures a high level of quality and at the same time fits into the concept of sustainable
development and Industry 4.0 by taking care of the following dimensions: economic
(economic) (improvement of quality, reliability, time, and cost of detection implementation),
environmental and social (minimisation of emissivity and energy intensity), modernity
(application of automation and digitisation), and image (customer loyalty and company
competitiveness). The developed model presents a way to increase the effectiveness and
efficiency of quality control through responsible management, automation, and digitisation
with care for the environment, which are factors that are lacking in the literature.
Practical verification of the effectiveness of the developed model for implementing
and maintaining an effective quality control system confirmed the correctness of its design
and effectiveness. The developed model provides the following benefits:
•
Organising and collecting detailed information on the characteristics of quality con-
trol methods;
•
Realisation of broad, multifaceted analyses of automated and semiautomated quality
control methods;
•Identification of the causes of quality problems and rapid response;
•Identification of critical non-conformities of aluminium castings;
•
Determination of the level of efficiency of quality control points and their continu-
ous monitoring;
•
Determination of adequate improvement measures (ensuring an increase in the effi-
ciency of quality control);
•
Optimisation in terms of key features of quality control methods: efficiency, efficiency
in the context of reliability, energy efficiency, emission efficiency, time efficiency, and
cost efficiency;
•Bringing enterprises closer to the concept of Industry 4.0;
•Ensuring a tight quality control system;
•Constantly striving to improve quality control methods and overall growth;
•Making sure that no resources, time, and potential are wasted.
The developed model is characterised by a high level of universality, which contributed
to a small number of limitations in its application. There were three main limitations:
•Implication of the model in relation to quality control by NDT methods;
•
Implication of the model in relation to automated and/or semi-automated quality
control methods;
•
Implication of the model in relation to production processes in which castings are made
of electrically conductive materials: metals and their alloys and some composites;
•The need for access to historical quality control data.
The scope, consistency, and accuracy of the presented implementation of the model
and maintenance of the concept of an effective quality control system make it possible
to apply this topic to other manufacturing enterprises for development purposes. The

Sustainability 2025,17, 1418 29 of 34
implementation of the presented analysis can greatly assist in the creation of develop-
ment strategies.
Future research directions will focus on integrating the proposed indicators with
real-time data analysis, which is key to the Industry 4.0 approach. The next research step
will be to develop software based on the assumptions of the model. Work will also be
carried out on extending the model to include optimisations based on artificial intelligence
to obtain better results.
5. Conclusions
The model of an effective quality control system for aluminium castings using indi-
cator analysis presented in this paper supports improvement and optimisation activities
of quality control points, which are closely related to ensuring a stable quality level of
manufactured products. Activities carried out in accordance with the assumptions of
the developed model are in line with the concept of sustainable development of enter-
prises (care for the environment, economy, and society) and should lead to the adoption
of the manufacturing model called Industry 4.0 as a result of the use of feedback between
automation, processing, and data exchange.
The developed model for ensuring an effective quality control system is based on
awareness and responsibility for the company’s activities that affect the ecology, economy,
environment, and society in relation to the control methods used. The model makes it pos-
sible to carry out diagnostics of aluminium castings at various stages of production using
diagnostic tests (non-destructive testing), leading to internal and external benefits for the
enterprise. Quality control in the foundry industry is characterised by an ever-increasing
collection of data of a numerical nature. Therefore, the data must be processed and handled
in a skilful way, which is made possible by the indicator analysis used in the model. The
developed model makes it possible to shape the checkpoints in the context of the increase
in the parameters of the key features of detection methods (effectiveness, reliability effi-
ciency, energy efficiency, emission efficiency, time efficiency, and cost efficiency). Practical
implementation of the model made it possible to draw attention to the benefits of the skilful
use of indicator analysis and automation of the quality control of aluminium products,
which include elimination of errors in process handling, faster implementation of detection,
increase in productivity, increase in operational capacity, and reduction in quality control
costs. The benefits gained from the application of the model are closely linked to the idea
of sustainable development and the Industry 4.0 paradigm. The model is characterised
by multifaceted analyses and is universal in respect to the research subject: aluminium
alloy products.
Future research directions will be related to the expansion of the model of an effective
quality control system with the analysis of additional features of detection methods relevant
to manufacturing enterprises. Further work will also be related to the development of
model-based software and its integration with detection machines, which will enable the
real-time tracking of partial performance parameters.
The developed model for implementing and maintaining an effective quality control
system can greatly assist in creating development strategies that optimise quality control
costs while ensuring the quality of the products offered.
Author Contributions:
Conceptualization, A.P. and K.C.; methodology, A.P., G.O. and K.C.; software,
A.P., G.O. and K.C.; validation, A.P. and K.C.; formal analysis, A.P.; investigation, A.P. and K.C.;
resources, A.P. and K.C.; data curation, A.P., G.O. and K.C.; writing—original draft preparation, A.P.,
G.O. and K.C.; writing—review and editing, A.P. and K.C.; visualization, K.C.; supervision, A.P., G.O.
and K.C.; project administration, A.P. and K.C.; funding acquisition, A.P., G.O. and K.C. All authors
have read and agreed to the published version of the manuscript.

Sustainability 2025,17, 1418 30 of 34
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
No new data were created or analysed in this study. Data sharing is
not applicable to this article.
Conflicts of Interest: The authors declare no conflicts of interest.
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