New Challenges for Quality Assurance of Manufacturing Processes in Industry 4.0

Abstract

Nowadays the flexibility and specific cost of manufacturing have a relevant role in the competitiveness of the companies. In our opinion the most important objective of Industry 4.0 is the realization of intermittent manufacturing at mass production’s productivity and specific cost. This aim can be only reached by creating more complex manufacturing systems. The increase in manufacturing complexity results in new challenges in the quality assurance of manufacturing processes. We can collect new types of data that enable the improvement of product and process service quality. This paper introduces the essence of Industry 4.0, as well as the new challenges for the quality assurance of manufacturing processes. Possible research directions for overcoming challenges are also presented.

Full text

New Challenges for Quality Assurance of Manufacturing Processes
in Industry 4.0
Béla Illés1,a, Péter Tamás1,b, Péter Dobos1,c, Róbert Skapinyecz1,d
1Institute of Logistics, University of Miskolc,
H-3515, Miskolc, Miskolc-Egyetemváros, Hungary
aaltilles@uni-miskolc.hu, balttpeti@gmail.com, cpeterdobos30@gmail.com,
daltskapi@uni-miskolc.hu
Keywords: manufacturing, quality assurance, Industry 4.0
Abstract: Nowadays the flexibility and specific cost of manufacturing have a relevant role in the
competitiveness of the companies. In our opinion the most important objective of Industry 4.0 is the
realization of intermittent manufacturing at mass production’s productivity and specific cost. This
aim can be only reached by creating more complex manufacturing systems. The increase in
manufacturing complexity results in new challenges in the quality assurance of manufacturing
processes. We can collect new types of data that enable the improvement of product and process
service quality. This paper introduces the essence of Industry 4.0, as well as the new challenges for
the quality assurance of manufacturing processes. Possible research directions for overcoming
challenges are also presented.
Introduction
The satisfaction mode of the unique customer needs significantly influence the
competitiveness of companies. The objective of Industry 4.0 is the satisfaction of unique customer
needs with the specific costs of mass production [1]. Complexity of manufacturing processes
significantly increases with expansion of the product types to be manufactured, which requires the
elaboration of new solutions in the fields of improvement and quality assurance of processes. This
can provide the basis of several research topics [2,3]. This objective can seem futuristic, but we can
reach this through the continuous development of technology. The cyber-physical systems and the
big data conception have created some new possibilities in the improvement of product and service
quality [4]. We can collect and process data for the manufacturing processes that has not been
gathered so far. We can determine the correlations among these data in order to make several
requirements regarding the forecast of failures of the products, material handling equipment or the
technological equipment. We have to discover the data types to be recorded and determine the
requirements for data recording, as well as elaborate possible methods and procedures for
evaluation and applications of the data. This paper introduces the characteristics of the industrial
revolutions and covers the most important tools of the 4th industrial revolution. Afterwards the
currently applied methods and procedures will be presented in the field of quality assurance of
manufacturing processes. In our opinion, quality-centric improvement will be more efficient using
tools of the Industry 4.0. The paper will explain these new improvement possibilities.
Formation of the Industrial Revolution
Basically, industrial revolutions are related to social, economic and technological changes. The
appropriate economic and social environment is necessary for the invention and spread of
technologies. We can define the beginning of the first industrial revolution from the invention of the
steam engine. Characteristics of the industrial revolutions are introduced in Table 1. Nowadays,
cyber-physical systems have been created as a result of the increase in the cohesion of the
information technology and automation. This new technology has induced the beginning of the
Industy 4 [5, 6]. Table 1 introduces several important characteristics of the industrial revolutions.
Solid State Phenomena Submitted: 2017-03-31
ISSN: 1662-9779, Vol. 261, pp 481-486 Revised: 2017-04-25
doi:10.4028/www.scientific.net/SSP.261.481 Accepted: 2017-04-26
© 2017 Trans Tech Publications, Switzerland Online: 2017-08-21
All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans
Tech Publications, www.ttp.net. (#98739296-22/08/17,18:18:34)

Table 1. Industrial revolutions [5]
Industrial revolution 1
Beginning: 1760s
Most important features:
- steam engine,
- mechanisation of textile plants,
- steamships,
- steam railway, etc.
Industrial revolution 2
Beginning: 1870s
Most important features:
- electricity,
- oil industry,
- steel industry,
- invention of internal combustion engine,
- mass production, etc.
Industrial revolution 3
Beginning: 1930s
Most important features:
- nuclear power,
- new technologies,
- CAD/CAM systems,
- CIM systems, processes, networks, etc.
Industrial revolution 4
Beginning: from today
Most important features:
- Internet of Things (IoT),
- Cyber-physical systems,
- logistics 4.0, manufacturing 4.0,
hospital logistics 4.0., etc.
Most Important Tools of Industry 4.0
Nowadays Industry 4.0 has numerous elements, of which the most important are the Internet of
Things (IoT), cyber-physical systems and big data. These tools will transform the whole world,
according to a number of experts [2, 4]. The section presents an explanation of these tools.
Most important tools of the 4th industrial revolution:
- Internet of Things (IoT): This term was first used in 1999 by Kevin Ashton [7]. IoT enables the
access of different equipment through the internet/some networks, as well as in certain cases the
communication between equipment. In recent decades people have recorded the majority of the
data that we can find on the internet. In essence, this has significantly determined the quantity of
available data. For more efficient improvement of logistics systems we need to collect more
information about the systems’ components (e.g. products, machines, material handling
equipment, humans, etc.) with use of the IoT. On the basis of these data we can analyse more
information about our system and we can optimise it more efficiently. For example, if we place
some sensors on important parts of technological equipment that will send signals on the status
of the parts, then we can get information before the failure of the technological equipment.
- Cyber-physical systems [8]: The development of informatics and automation, as well as their
increasing cohesion, have enabled the application of cyber-physical systems (if an electronic
device contains a control and network connection then we can call this system a cyber-physical
system). These systems are able to collect data from their environment, and after analysing these
data they are able to modify their positions. Cyber-physical systems are connected through a
network, and their significant parts are also connected with each other; because of this we can
apply swarm intelligence, which can result in more efficient work.
- Big Data concept [4]: The amount of data in the world approximately doubles every two years
[6], which results in huge amounts of data in the different areas of life (astronomy, logistics,
trade, the stock exchange, etc.). We can create new services and useful conclusions with the
elaboration of the data’s correlations. An example of such a service is forecasting flight prices
with software that is able to determine the estimated flight price on the basis of the previous
period’s data (in this case it is not necessary to know the process of the price determination).
Big data’s essence is determination of probabilities with mathematical methods and procedures.
According to many experts, big data will significantly change the future; with it we can make
decisions on the basis of the huge amount of data without knowing the causes and effects.
The expressions which were explained in this section are related to each other. We cannot speak of
cyber-physical systems and big data without IoT.
482 Precision Machining IX

Currently Used Methods in the Quality Assurance of Production Processes
In order to achieve the quality oriented control and management of manufacturing processes,
most companies create a management system for the implementation of the goals derived from the
quality policy. The quality goals are realised through quality design, quality assurance, quality
control and quality improvement [9]. A non-exhaustive list of the numerous tools and methods
belonging to these areas is represented in Figure 1.
Quality management
Quality design
CRM, QFD,
Benchmarking,
etc.
Quality assurance
Poka-yoke, Andon,
Jidoka, 5S,
Standardization,
Quality standards,
etc.
Quality control
SPC,
Auditing,
TQC,
etc.
Quality
improvement
Six sigma,
Kaizen; trainings,
BPR, etc.
Quality goals Quality policy
Figure 1. Classification of quality management tools and methods used in relation to manufacturing
systems [9]
Quality design [9]: The measures and activities which define the quality goals and, based on these,
make possible the planning of the processes of implementation and of the required resources.
- CRM: CRM (Customer Relationship Management) is a comprehensive methodology aimed at
the handling of customer relationships and is increasingly based on the utilisation of big-data
analysis and special software tools. It enables the effective identification of the customer’s
quality requirements regarding the given products and services, among other possible uses.
- QFD: QFD (Quality Function Deployment) is a well-known method for the systematic
transformation of the customer’s requirements into concrete technical and performance
parameters, thereby allowing their integration into the design process.
- Benchmarking: The aim of benchmarking is to systematically reveal and implement the best
practices. The search can be conducted at a given organization or in an entire sector (internal
and competitive benchmarking), or by transcending these boundaries and instead focussing
either on functionality or on an entire problem (functional and generating benchmarking).
Quality assurance [9]: The measures and activities aimed at increasing the confidence that the
quality requirements will be met.
- Poka-yoke [12]: Poka-yoke includes simple technical solutions based on auto-detection that can
be integrated into the manufacturing processes and can rule out the occurrence of failures
originating from human negligence.
- Andon [12]: The concept of Andon includes visual signalling devices (lamps, screens, etc.) that
show if a problem has occurred in relation to a manufacturing process (in parallel, the process
stops until the problem is fixed).
- Jidoka [12]: Jidoka relates to a certain automation philosophy that separates the manned and
automated working processes in order to achieve greater efficiency. Moreover, in the Jidoka
concept the technological devices automatically stop in case of the detection of a failure.
- 5S: 5S is a standard workplace organisation method which got its name from its five basic
principles (Seiri - sort, Seiton – set in order, Seiso - shine, Seiketsu - standardize, Shitsuke -
sustain).
- Standardization: Standardization of the processes and work methods at the given company.
- Quality standards: The most widely used series of standards in the field of quality management
is the ISO 9000, especially the ISO 9001 standard.
Solid State Phenomena Vol. 261 483

Quality control [9]: The measures and activities aimed at fulfilment of the quality requirements.
- SPC: SPC (Statistical Process Control) is one of the most basic tools in quality control that
enables the precise tracking of the quality performance of industrial processes and the proper
timing of the necessary interventions, using sampling based control.
- Auditing: One of the main goals of auditing is to verify the compliance of the processes of the
company with the quality standards, moreover with the requirements defined by the
organization’s own quality system. As a result, auditing is also one of the basic tools in the
implementation of quality control.
- TQC: The essence of the concept of TQC (Total Quality Control) is that it extends the scope of
quality control to the entire company, moreover to the entire product life cycle. This is achieved
through the involvement of all the departments of the organization, thereby exceeding the
traditional boundaries of manufacturing.
Quality improvement [9]: The measures and activities aimed at increasing the potential quality
performance.
- Six Sigma: Six Sigma is a quality improvement methodology which is in many ways built upon
the SPC, with the aim of improving the manufacturing processes through the minimization of
failure rates and the reduction of performance fluctuations (DMAIC method).
- Kaizen: An infinite process comprised of small improvement steps, based on utilizing the
creativity of the workforce for improving the processes.
- Trainings [10]: The training of the workforce plays a crucial role in the effective
implementation of methodologies such as Six Sigma and Kaizen, and in the successful
application of almost all quality management tools in general.
- BPR: BPR (Business Process Reengineering) puts the emphasis on the radical redesign of
fundamentally defective processes.
New Challenges for the Quality Assurance of Manufacturing Processes in Industry 4.0
By applying currently implemented equipment and methods of the fourth industrial
revolution, it becomes possible to collect data that has been unknown up to now. This leads to new
opportunities in the quality field regarding production processes. Certainly, the range of
opportunities will become even wider through developing new technologies and methods. Soon we
will be capable of making a reliable forecast about the errors of material handling machines,
technological equipment and products. Therefore, we will be able to prevent them. By using cyber-
physical systems, big data and artificial intelligence, the efficiency of running a flexible production
system will increase continuously due to the following:
- minimization of material handling routes,
- decrease in failure feasibilities,
- setting up optimal production planning,
- ensuring communication among production system objects (material handling machines,
technological equipment, products, employees, etc.).
The most important question is to define where, how and what kind of data need to be collected and
how they shall be used. As a matter of fact, the needed information is available in the environment
of objects applied in production and in production areas as well. In the following part, we will
present examples of the important data that need to be collected and the possibilities to use them.
- Material handling machines [3]: The goal of running material handling machines is to manage
logistics tasks with a maximum of availability and a minimum of material handling routes/time.
Collection of the following new information can support this:
 route temperature (it influences duct planning),
 environment temperature, humidity (influences duct planning),
 forces exerted on material handling equipment and products to be delivered (influences
duct planning),
 atmospheric pressure of tires (influences duct planning),
 life time of parts (influences maintenance planning),
484 Precision Machining IX

 monitoring the frequency of vibration of determined parts (influences maintenance
planning),
 monitoring moisture by determined parts (influences maintenance planning).
- Technological equipment: The most important goal in the usage of technological equipment is
to ensure the maximal availability, as is also true for material handling equipment. In addition to
this, managing an adequate level of operation is also a focus of attention. Collection of the
following new information can support this:
 atmospheric pressure, current supply needed for the operation (it influences the ideal
way of equipment operation),
 environment temperature, humidity (influences the ideal way of equipment operation),
 forces exerted on the product (influences product quality),
 forces on the operating tool [11] (influences maintenance planning),
 life time of technological equipment parts (influences maintenance planning),
 vibration frequency by determined parts (influences maintenance planning),
 monitoring of moisture by determined parts (influences maintenance planning).
- Human resources: When applying human resources, an important goal is to ensure maximum
availability and carry out the work according to standards. Collection of the following new
information can support this:
 environment temperature, humidity, brilliance/light (influences work efficiency),
 speed of movements, acceleration, number of pulses (influences work efficiency),
 data belonging to the operation carried out (to check whether the standard requirements
have been met).
One consequence of collecting so-far unknown data is the need for implementing new data
collecting equipment and online data transfer equipment. This equipment needs to be attached to the
raw material or to the person/machine carrying it.
Expectations for data collection equipment [3]:
- Attachment method must not influence the raw material quality,
- Resistant to damage,
- Possibility of recycling,
- Ability for continuous data transfer,
- Ensuring the possibility of data transfer over long distances,
- Ensuring built-in charger.
Summary
The paper has given an overview about the formation, important equipment and challenges of the
fourth industrial revolution in the field of quality assurance. It can be stated that production system
operation problems can be reduced significantly and product quality can be improved by using new
– so far not collected – data generation and by application of the big data conception. Important
research work in this field is to define the data types that have an influence on more effective
operation of production systems, to develop technology for data collection, and to adapt of
determined conclusions in cyber-physical system operation. In connection with these, we have
described some of the important data types that need to be collected and also their application
possibilities.
Acknowledgement
“The described article was carried out as part of the EFOP-3.6.1-16-00011 “Younger and Renewing
University – Innovative Knowledge City – institutional development of the University of Miskolc
aiming at intelligent specialisation” project implemented in the framework of the Szechenyi 2020
program. The realization of this project is supported by the European Union, co-financed by the
European Social Fund.”
Solid State Phenomena Vol. 261 485

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