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From IoT-based cloud manufacturing approach to intelligent additive manufacturing: industrial Internet of Things—an overview

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The industrial Internet of Things (IIoT) has grown to empower advantages of advanced manufacturing machinery and smarter control. The cloud-based technology of remote data collection, intelligent machine interconnectivity, and sensor monitoring provide the opportunity for a pattern modification across all manufacturing divisions including the latest and rapidly growing technology of additive manufacturing (AM) or 3D printing. AM is a type of direct manufacturing and revolutionary technology that enables complicated production and a formation which can shorten manufacturing processes and supply chain procedures. Data is a key factor in the age of big data, embedded IIoT solutions, and services in the new machinery and mechanism that bring an additional capability to integrate and manage data streams within the Internet of Things (IoT) cloud-based platform. Movement and merging conventional (legacy) manufacturing technology into the shared and modern machinery required for the state-of-the-art manufacturing technology such as AM is complex and challenging, but it needs to be organized and fixed resourcefully, while it remains linked, flexible, and scalable. AM is an advanced manufacturing system and technology involving the new era of complex machinery and operating systems. AM has been identified as a special value to the industry which has many applications in the different industries such as aerospace, medical and healthcare, energy, and automotive. Hence, high-performance computation and processing will be very important in AM. This research takes an overview of the cloud-based model and concept of cloud computing (CC), cloud manufacturing (CM), IoT, and their relations and influences in the AM industry 4.0 era. This study contributes as a theoretical basis and as a comprehensive framework for AM integration. Furthermore, this paper presents CM applications and integration with AM and proposes an integrated AM cloud platform.
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From IoT‑based cloud manufacturing approach tointelligent additive
manufacturing: industrial Internet ofThings—an overview
LidaHaghnegahdar1· SameehanS.Joshi1,2· Narendra B.Dahotre1,2
Received: 24 August 2021 / Accepted: 22 November 2021
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
The industrial Internet of Things (IIoT) has grown to empower advantages of advanced manufacturing machinery and smarter
control. The cloud-based technology of remote data collection, intelligent machine interconnectivity, and sensor monitoring
provide the opportunity for a pattern modification across all manufacturing divisions including the latest and rapidly grow-
ing technology of additive manufacturing (AM) or 3D printing. AM is a type of direct manufacturing and revolutionary
technology that enables complicated production and a formation which can shorten manufacturing processes and supply
chain procedures. Data is a key factor in the age of big data, embedded IIoT solutions, and services in the new machinery
and mechanism that bring an additional capability to integrate and manage data streams within the Internet of Things (IoT)
cloud-based platform. Movement and merging conventional (legacy) manufacturing technology into the shared and modern
machinery required for the state-of-the-art manufacturing technology such as AM is complex and challenging, but it needs
to be organized and fixed resourcefully, while it remains linked, flexible, and scalable. AM is an advanced manufacturing
system and technology involving the new era of complex machinery and operating systems. AM has been identified as a spe-
cial value to the industry which has many applications in the different industries such as aerospace, medical and healthcare,
energy, and automotive. Hence, high-performance computation and processing will be very important in AM. This research
takes an overview of the cloud-based model and concept of cloud computing (CC), cloud manufacturing (CM), IoT, and
their relations and influences in the AM industry 4.0 era. This study contributes as a theoretical basis and as a comprehensive
framework for AM integration. Furthermore, this paper presents CM applications and integration with AM and proposes
an integrated AM cloud platform.
Keywords Internet of Things· Industrial Internet of Things· Cloud manufacturing· Additive manufacturing· Machine-to-
machine (M2M)· Industry 4.0
1 Introduction
Information technology (IT) is changing manufacturing
globally by moving into digital and complex modern manu-
facturing processes that can be termed as “smart manufac-
turing” or “Industry 4.0” [1]. Additive manufacturing (AM)
technology impacts on the fourth generation of the industrial
revolution is more than other manufacturing technologies
[2]. The Internet of Things and Services (IoTS) is one of the
key components of Industry 4.0; this enables service provid-
ers to offer their services throughout the Internet [2]. Today,
Industry 4.0 using enabled key technologies for great poten-
tial on resource efficiency improvement at the network level
shares the dynamic AM services in a cloud manufacturing
(CM) environment [3]. The AM system continues to undergo
major transitions due to emerging new technologies and con-
stantly changing market demands [4]. Through these transi-
tions, the AM system is likely to continue to severely reshape
by uncertainties and new demands by globalization and
growing use of the Internet [5]. Generally, a standard fabri-
cation channel operates in most AM procedures. Haseltalab
and Yaman [6], however, referred to a different fabrication
pipeline, which is improved and implemented as a CM
method. The method is a combination of several operations
* Lida Haghnegahdar
1 Center forAgile andAdaptive Additive Manufacturing
(CAAAM), University ofNorth Texas, Discovery Park, 3940
N Elm St, Denton, TX76207, USA
2 Department ofMaterials Science & Engineering, University
ofNorth Texas, Denton, TX, USA
/ Published online: 3 January 2022
The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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gathered to accomplish the pre-fabrication processes as well
as the printing task [6]. According to the ASTM Interna-
tional F42 Committee [7], AM is the set of processes of
materials combination that makes parts from 3D model data,
usually layer-by-layer manner. It involves integration of vari-
ous types of datasets starting with the design file, conversion
of the design file into the printer readable file, data generated
by the printer and onboard diagnostic sensors, and finally the
data generated during post-production analysis of the parts
[8]. This is a novel manufacturing methodology and provides
a one-step computer-controlled production of near-net-shape
components of complex geometries enabling rapid product
development. AM has proved to be a paradigm shift in the
manufacturing arena which has conventionally relied heav-
ily on supply chains, usage of fasteners/joining processes to
assemble multiple parts together within a component, and
conventionally available materials limiting its capabilities
in further enhancing the component functionality and effi-
ciency. Furthermore, the emerging CM pattern can facilitate
access to various AM resources with minimum investment.
CM is expected to focus on supporting the AM process and
the needs of customers throughout the production process
development, from designing to printing rather than provid-
ing simple 3D printing services [9].
CM has been investigated for two applications in manu-
facturing such as AM. The first application is related to the
(AM) manufacturing process involving CM-based embed-
ding, connection, and operation of the current manufactur-
ing resources (e.g., AM machines/3D printers, and relevant
devices). In this regard, AM can be utilized inside a CM con-
ception. The next application is relevant to end-user services
that use Internet-based AM/3D printing resources instead of
utilizing their on-site 3D printer [10, 11]. Such a remote as
well as in-site integrated advanced additive manufacturing,
for its efficiency must adopt a manufacturing system man-
agement (MSM) in CM approach, which includes resource
perception, distributed resources, connection, digital cat-
egory, searching, matching, inclusive evaluation, service
selection, configuration, scheduling, transactions, service
network, security, and application system [12]. MSM will
link all the Internet of Things (IoT), big data (BD), and cloud
computing (CC) in CM [12].
The CM platform can connect an internet-based service
platform with AM that is close to the standards and value
of Industry 4.0. CM platform is capable of integrating soft-
ware applications and digital services into the manufacturing
(process and parts or components) throughout a Web-based
environment to give access to both the service provider and
the user via the Internet [2]. The cloud platform can schedule
selected major tasks for all the connected and distributed
3D printers. These tasks may include group parts schedul-
ing at each print (uploading component drawing) or placing
each part in the printing process for each print. This task
list needs to be scheduled using a scheduling algorithm to
perform flexible and quick responses to any changes for cus-
tomers when they want to add, edit, or delete tasks for print-
ing [13]. Recently, some studies have been conducted on
each part of the AM process. There are some limitations and
weaknesses in AM which are relevant to some arrangements
or prefabrication time such as designing CAD model, STL
file conversion, slicing, setting the fabrication parameters,
and G-codes [6]. Researchers try to decrease and modify
these limitations using new technology applications such as
CM capabilities.
CM has been signified as a development path to future
manufacturing, the way to provide flexible and scalable
solutions for legacy machinery and manufacturing to com-
panies. This helps in sharing manufacturing resources into
on-demand manufacturing services [2, 15]. with lower main-
tenance costs. CM as a service system is being considered
for the development of an “Internet-Web-based” to boost
machine accessibility, monitoring, and process planning
[16]. These will radically renovate manufacturing lines that
in turn will result in a more resilient and sustainable AM
CM is an intelligent and knowledge-based platform [14]
that can increase manufacturing sustainability and effi-
ciency through the process and production cycle in four
ways. These ways include shared design, better automation,
enhanced process resilience, and decreasing waste or repro-
cess. Figure1 presents a proposed graphic of an integrated
AM with the intelligent cloud.
The rest of this paper is arranged to cover cloud platform,
cloud potentials and challenges in manufacturing, cloud
innovations in additive manufacturing in Sects. 2, 3, and 4,
respectively, and conclusions are discussed in Sect.5.
2 Cloud platform
The cloud platform is a platform as a service (PaaS), a solu-
tion for changing software production, distribution, con-
sumption, and pricing. This platform is rising to develop
on-demand functions and applications to change the prevail-
ing IT models in the industry [17]. Cloud service describes
cloud technology service “as-a-service” through the IoT
2.1 Cloud computing
Scalable and efficient database management systems aligned
with new data management architectures have been devel-
oped for CC [19]. Cloud data management consisting of
cloud, query language, execution environment, storage, and
infrastructure will be monitored within a layered architecture
1462 The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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CC is a combination of computer technology and an
influential pattern for IT-based solutions presence. CC can
transform applications and systems from a product-centric
base into a global, distributed, and service-centric base. Cor-
porations must focus on cloud plans rather than technol-
ogy, which will help them understand cloud services and
make investment decisions better. CC has capabilities such
as controlled interface, location independence, sourcing
independence, universal and virtual access, traceability, and
rapid elasticity [20]. CC is a combination of an on-demand
shared pool and configurable computing resources. CC is
transforming processes, networks, and business scope and
developing dynamic capabilities [21, 22]. CC is a disruptive
technology that provides an alternative to or helps in-house
IT services [23].
Generally, two suggestions for CC implementation in
manufacturing are considered: Industry 4.0/smart manufac-
turing and CM. The first one adopts cloud services partially,
while the second one is supported through cloud services
2.2 Cloud manufacturing
CM is a service-oriented and distributed manufacturing net-
work with enabled manufacturing modeling which is devel-
oping rapidly. It supports transforming from production-
based manufacturing into a service-based process of the CM
system [24], an approach highly suited for AM technology.
This structural concept and model describe the functional
manufacturing subsystems. CM is an integrated structure
Fig. 1 Additive manufacturing integration with intelligent cloud presented with examples of laser powder bed fusion and laser directed energy
deposition systems along with their subcomponents
1463The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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of CC and IoT that provides the virtualized manufacturing
resources with a service-oriented tendency. The most impor-
tant issues that can be considered in CM include planning,
control systems, flexibility, resource combination, business
models, data compatibility, automation, security, data pro-
viders, coordination between machines, automation, and
efficient collaboration.
CM adopts and extends the CC concept to transform
manufacturing processes into componentized and integrated
manufacturing capabilities with global optimized resources.
Erwin Fielt etal. [25] states manufacturing capabilities in
terms of cloud service and present a manufacturing perspec-
tive based on CM. To support two types of cloud users—
customer and enterprise users—service methods have been
developed with consistent data models to describe cloud
service. Cloud technology apart from resource sharing and
cost-efficiency bring several benefits to the manufacturing
industry such as directness and production scalability [26].
Smart CM is the next evolutionary stage of CM based on
the ubiquitous arrangement that includes Internet, mobile
network, IoT, telecommunication network, digital net, and
information technology. All services are controlled in a
smart way to provide timely demand response services and
manufacturing accomplishments for customers. Manufactur-
ing abilities and resources must be identified in the cloud
as dependable manufacturing services to implement CM.
Operational processes, from design to manufacturing, are
integrated as CM services [26]. One of CM’s typical charac-
teristics is on-demand use, which pays for the manufacturing
capability. So, it is so important to consider the manufactur-
ing cost related to CM’s cloud service. The different aspects
of this cost in CM includes the cost of the entire CM’s cycle
cloud service.
Manufacturing resources in CM are composed of vari-
ous hardware and software resources, human and knowl-
edge resources, and capabilities. The whole CM cycle cost
includes operation cost, consultation cost, market analysis,
business outsourcing cost, purchase cost, logistics trans-
portation cost, handling cost, tax, and product and service
inspection cost [27]. The important benefits of CM are
related to the manufacturing capabilities that include service
modeling, management, and evaluation of the demand and
supply balancing. These capabilities can be utilized in data
management and renewable integration fields. Changing the
mix of these capabilities can develop cloud structures into
achieving practical and sustainable benefits.
CM provides various solutions that can overcome the
barriers that prevent sustainable manufacturing. Figure2
describes cloud platform components and solutions in
additive manufacturing. The main two highlighted bar-
riers include lacking proficient set knowledge and access
difficulties related to the accessible manufacturing envi-
ronment and processes information [28]. CM has two
main characteristics including knowledge and shared data.
Data is collected, stored, and shared throughout the sup-
ply chain. Data accumulation [29], data integration and
then data streams in the cloud is challenging because data
sharing should show what data is shared, how much, and
with whom.
CC emergence in the past few years is one of the sig-
nificant advances in the computing forum. From a manage-
rial perspective, the cloud is looking for lower prices with
customer concerns. CC also presents the key characteristics
and high potential for its adaptation in the CM concept. This
paper mostly tries to select publications from the basket of
journals to study and refer to the CC, CM, and IoT (Table1).
A literature review indicates that CM is a structural concept
that is likely to be realized into the free operation and trans-
mission, full-scale sharing, and on-time demand response
for the various manufacturing resources and capabilities in
the manufacturing service procedure. Cloud-based manu-
facturing technology [30] integrates machine to machine
(M2M), IoT, big data, and CC to accelerate communica-
tion and data stream (Fig.3). Big data helps monitoring and
examination of the process components at each step. Big
data evaluation helps to analyze AM processes and facilitate
AM in supply chains [28]. M2M facilitates communication
and information exchange between machines. Limited data
sharing may occur between cloud users and customers at
the initial steps, but data sharing may increase trust dimen-
sions while relationships are progressing [14]. CM is CC
and is part of a “smart technology” term. This technology
is capable of adapting system modification [30]. The main
goal of CM-CC is to deliver highly reliable, scalable, and
available computing services while working non-stop in a
distributed industrial environment [29]. Table2 lists CM
characteristics [14] and their applications. Machine automa-
tion, monitoring, optimization, optimization integration [31,
Fig. 2 Cloud platform components and solutions in additive manufac-
1464 The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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32], and comprehensive situational evaluation and awareness
are needed to support cloud-based technology [29] (Fig.4).
2.3 IoT
IoT helps to address and resolve manufacturing challenges
potentially. IoT and CM are interlinked though some work
only focuses on one of them and considers the other one
as a principal technology unit. Implementation in a highly
service-driven manufacturing system that involves sys-
tematic CM support is hard to respond to the real-time
dynamics captured from the IoT-enabled execution hier-
archy. CM can adjust resources increasing or decreasing
dynamically according to the cloud users demand [3335].
CM manages and schedules resources dynamically to sup-
port manufacturing resources effectiveness [36, 37]. CM
Table 1 Selected studies from the literature focusing on the cloud-based manufacturing
Author reference Summary
(Iyer etal.) [20] This article considers seven cloud capabilities and how they can respond to current challenges in the marketplace
(d’Orazio etal.) [19] This paper presents a multidimensional cloud architecture to store data and optimize storage costs
(Cheng etal.) [27] This study investigates the CM service cost constitution to determine the CM practical operation and the lowest cost
(Marston etal.) [22] This research studies CC as one of the major advances in the computing history era
(Popeanga) [57] This work counted several considerable indexes such as the low cost, flexible, fast response time, and functionality to
mention some CC potentials such as security, implementation, and functioning that is required for large-scale smart
network applications
(Wang etal.) [9] This study describes cloud service and manufacturing capabilities perspective based on CM and discusses cloud
architecture, and technologies that can enhance CM
(Markovic etal.) [84] This work discusses cloud technologies and CC services as a solution to address large-scale real-time computing better
(Yigit etal.) [59] This study introduces CC terms and their applications such as security, smart network, and usability
(Tao etal.) [60] This paper investigates intelligent perception through IoT applications in CM and manufacturing resources and services
classification and their relationship with it
(Song etal.) [54] A CM service platform has been considered to find the impacts on small- and medium-sized business improvements
(Zhang etal. ) [86] This study investigates cloud service and the constructing method for manufacturing cloud
(Tao etal.) [48] This paper by studying IoT and cloud tries to propose a new method as an intelligent connection such as man-to-
machine, machine-to-machine, and on-demand procedure and resourceful sharing
(Bera etal.) [85] This study counted the current research problems which are related to cloud-based systems and smart systems
(Tao etal.) [12] This study considers manufacturing service quality and CM to find various manufacturing capabilities such as resource
sharing and free circulation
(Qu etal.) [83] This paper focuses on modern CM and IoT infrastructures to find how their integration can enable a smart dynamic PL
synchronization and dynamic control mechanism
(Battleson etal.) [21] This study considers CC and dynamic capabilities development to look for system processes transformation and
propose a novel insight
(Giessmann etal.) [17] This paper develops a theory for designing cloud software platforms and discusses the transforming, configuring, and
calibrating challenges that platform providers may face with
(Ren etal.) [61] This paper considering key characteristics of CM supports a detailed review of relevant concepts in CC and advanced
manufacturing technologies that play an important role in the CM evolution
(Liu etal.) [66] This study proposes a game theory model for resource-shared CM. A Case study was used for satisfaction and utility
(Bai etal.) [93] This work depicts a game theory-based CM for multi-job optimization scheduling
(Wang etal.) [13] This paper by a two-step solution presents an intelligent AM system in a cloud-based AM environment
(Xiao etal.) [92] This study uses an optimization biogeography algorithm for multi-task scheduling in CM based on a game theory
(Haghnegahdar) [81] This research uses an optimization method to apply through the decentralized cloud modeling to enable smart grid
(Wei etal.) [87] This work proposes a CM-based product platform including five layers: resource, cloud technology, cloud service,
application, and user layers. Moreover, it considers some technologies for enabling and forming the product platform
(Carlucci etal.) [90] This research represents a minority game theory approach to support simple task quantity and service/resource
allocating in CM
(Zhang etal.) [91] This work considering some comparative experiments shows a game theory-based method for utility-aware multi-task
scheduling in CM
1465The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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enables handling of the data related to design, feedstock
properties, and data generated during manufacturing includ-
ing log data and diagnostics sensor data. CM-based addi-
tive manufacturing has enabled remote manufacturing and
is disrupting the conventional supply chain-based system
utilized in conventional manufacturing industries [32, 33].
Additionally, a process-driven manufacturing platform
such as additive manufacturing which integrates multiple
functions together requires CM support at various stages
from design to actual production of the part. According to
the published report by the International Data Corporation
(IDC), by 2025, the worth of IoT operations and applica-
tions improvements would be over $470 billion per year.
IoT applications for manufacturing include equipment per-
formance optimization and monitoring, production qual-
ity control, and human-to-machine interface [34]. IoT is
getting a definite position in the industry by changing how
goods are manufactured, scheduled, distributed, serviced,
and refined [29]. IoT supports specific industrial services
such as data collection, data storage, and manufacturing
device data analysis to facilitate manufacturing processes
[29, 35, 36]. The huge potential of IoT applications require
sustained capital investment and improved innovations in
both services and applications [29].
Using cloud-based technologies is a relatively modern
idea. IoT services in the cloud are committed to mitigat-
ing risks such as demand, inefficiency, innovation, scaling,
and control. Furthermore, from an advanced manufacturing
point of view, IoT based services have enabled the process
monitoring required for ensuring the production of a part
without defects [9, 37, 38]. Additive manufacturing involves
multiple steps carried out by an automated system and an
error in execution may lead to wastage of time and resources.
IoT based systems can monitor the process and alert the user
preemptively. Additionally, an enabled IoT system is capable
of correcting the process on the fly to mitigate/eliminate
the risks associated with process defects. As an extension,
the IoT-based system can also be utilized for securing the
process from cyberattacks [39, 40].
2.4 IIoT
The emergence of machines with digitization, and other ele-
ments to communicate with the physical world resulted in
the manufacturing industry embracing IIoT technology. IIoT
brings higher levels of automation which reduces the cog-
nitive loads placed on the operator by remote monitoring.
Furthermore, the IIoT plays a “smart” role behind the scenes
in the control of the manufacturing system. The standard
IIoT systems architecture in the general form takes on the
framework and structure by merging existing functions and
adding on “smart” functionality to provide a self-controlling
feedback system [35, 36].
Emergence of IIoT is beneficial for the manufacturing
industry in the following aspects:
Fig. 3 Cloud-based manufactur-
ing technology integration
Table 2 CM characteristics CM characteristic Application
Flexibility and scalability Real-time data monitoring to optimize manufacturing system
Knowledge intensive Data utilization to optimize process conditions
On-demand Customization of products and make changes according to
the customer’s needs
Multi-tenancy Coordination with other several manufacturers’ excess flows
Manufacturing service Outsourcing or subcontracting some parts of the supply chain
1466 The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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Development of machines with communication and digi-
Approaching human–machine communication as a cyber-
physical system (CPS)
Embedding IIoT technology infrastructures such as 5G,
M2M, Industry 4.
Changing the manufactured pattern
Supporting logistic services such as scheduling, distribu-
tion, servicing, and product completion
IIoT and cloud applications not only can enhance manu-
facturing operations at all levels, but they will also be able
to optimize smart manufacturing production. Integrating
IIoT operations into the cloud is a complex and challeng-
ing process as manufacturers are required to pay atten-
tion to preparation of standardized planning to meet these
challenges. Nonetheless, the manufacturers will be able
to produce the components with high complexity in low
volumes or produce high volumes of products with low
complexity. The IIoT brings several benefits to manufac-
turers’ production lines. IIoT helps to improve operational
efficiency and productivity, reduce downtime, efficiency,
and cost-effectiveness. Furthermore, IIoT devices bring
several benefits to manufacturers’ production line devices
in which monitoring, collecting, exchanging, analyz-
ing, and delivering valuable insights can help to propose
smarter, faster process decisions. All subdivisions such as
manufacturing, logistics, energy utilities, and more can
connect users to the facilities driven by the IIoT. Manu-
facturing is by far the largest IIoT market.
2.5 Relationships betweenCC, IoT, CM, andIIoT
CM is the concept and model based on CC, IoT, and other
advanced technologies in the environment of informative
manufacturing. CM will be able to promote the manu-
facturing industry on the combined network, intelligence,
service-oriented platform, and thus, it will raise the AM
manufacturing-information to a desired level [22, 27]. IoT
and CC have been studied and applied in many subjects,
as they can interpret the intelligence and M2M connection
(including man-to-man, man-to-machine, and machine-
to-machine), along with on-demand use and resourceful
sharing. For advanced manufacturing operations, realizing
the change and transformation from production-oriented
manufacturing to service-oriented manufacturing is a key
bottleneck. The relations, interactions, and differences
between CC, CM, and IoT are explored and discussed in
CM consolidates IoT and CC to enhance operations,
security, efficiency, cost reduction, and service-oriented
manufacturing of AM that leads to creating the advanced
AM. Figure5 shows the AM cycle from designing step to
final products which can be implemented in the CM envi-
ronment through the relationship between CC and IoT. In
fact, CM utilizes this relationship to integrate manufactur-
ing resources in the AM process. The relationship between
CM, IoT, and CC is found in the mutual and shared influ-
ences between IoT for the intelligence of manufacturing
resources and capabilities, Internet of Services (IoS) based
on CC, and Internet of Users (IoU) for the applications
in the entire product life cycle [29, 41]. The focus point
of CM-CC is on choosing on-time computing services
through continuous and highly reliable operations in a
distributed industry environment. Table3 categorizes IoT,
CC, CM, and IIoT applications in manufacturing.
CM service provides the supporting systems that are
required to manage operations in a cloud-based manufac-
turing platform. CM services include [31, 34]:
Supporting multi-agent and data security
Performance optimization in manufacturing process
Remote real-time collaborative approach and integrated
Operation’s incident recovery
Data fusion, conversion
Access control for several users
Platform functions customization
Fig. 4 Requirements for running cloud
1467The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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3 Cloud challenges andpotentials
inadditive manufacturing
Additive manufacturing poses some unique challenges and
opportunities for the implementation of cloud-based sys-
tems. On the one hand, cloud-based systems are inherently
suitable for the highly integrated and automated process
such as additive manufacturing. Conversely, the imple-
mentation of cloud-based services remains proprietary and
process specific making the overall manufacturing opera-
tion vulnerable for the breaches and cyberattacks. In light
of this, the current section highlights advantages offered,
and unique challenges faced by cloud-based systems spe-
cific to the additive manufacturing process.
3.1 Challenges
CM mainly provides service support for managing trans-
actions and tasks in small- and medium-sized enterprises
(SMEs). It involves a virtual manufacturing system with
the improvement of the intelligent matching engine to
make a balance between demand and supply. CM controls
service resources in the next step to allow users to submit
their manufacturing service demands with a series of com-
plex manufacturing processes such as project matching
and automatic transaction management for accomplish-
ment [42].
CM technology is created based on the manufacturing
industry needs to become better united and connected. In the
leading edge of the CM concepts, there are some challenges
predictably, and the future development of key technology
will encounter the challenges [27]. CM is a model which
combines the use of emergent computer techniques with
advanced models for manufacturing. Manufacturing mod-
els are becoming closer to the network-related technologies,
which are often faced with cyberattacks and the consequent
requirements for security. The digital world opens the door
to broad cyberattack prospects crossing through community,
finance, and industrial domains. Many engineers are una-
ware of the current and future threats of cyberattacks and
Fig. 5 Relationships between
CC, IoT, and CM
Table 3 IoT, CC, CM, and
IIoT applications in (AM)
Monitoring On-demand computing
services with high reliability
Higher levels of automation and control, i.e., deep
Control Availability Reduces the cognitive loads placed on the operator
Big data and
Scalability Smart functionality that provides feedback system to a
system that is self-controlling
sharing and
Working non-stop in a
distributed industrial
Collaborative agents within the scenes of systemized
command and control
1468 The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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are subsequently unable to diagnose the cause effectively
when subjected to an attack [43]. Understanding existing
weaknesses in manufacturing and overcoming those should
be the first step towards preventing, detecting, and mitigating
cyberattacks. Specifically, most vulnerabilities in manufac-
turing are relevant to designing systems, production control,
quality control, and manufacturing cybersecurity research
weakness. The lack of such knowledge resulted in serious
challenges for the wide deployment and adoption of CM. To
remove this gap, the manufacturing system tries to investi-
gate the security of CM.
Product and service complexity on one side and glo-
balization on the other side are increasing. Manufacturers
are required to compete in a global market, so they need to
design, plan, and operate their facilities in different environ-
ments. Service complexity and system complexity both are
increasing, while low-cost manufacturing still is desired to
maintain profits. These complexities and challenges, such as
globalization, increasingly complex information technology,
and energy consumption have pushed companies to move
into cloud services applications [44].
3.2 Potentials
According to Popeanga [45], cloud technology involves
low cost, flexible and layered architecture, and high-speed
response time. These benefits make security, interoperability,
and performance more feasible for large-scale network appli-
cations such as AM. Flexible resources and shared services
in the network, parallel processing, and extensive access
are desirable features of cloud technology in advanced AM
applications [46]. One of the critical factors for CM imple-
mentation is how to realize an intelligent insight and how to
access manufacturing resources [47]. CM is emerging as a
service-oriented manufacturing paradigm and an integrated
technology that promises to transform today’s manufactur-
ing industry to become highly connected, collaborative, and
innovative [4853].
IoT-based cloud has been introduced as a fully managed
service which is able to manage a complete IIoT solution.
IoT brings value through industrial manufacturing systems
by optimized solutions, predictive maintenance analysis,
and quality monitoring. IoT proposes a more secure con-
nection and environment for the users and customers to
manage industrial data and protected devices. Industrial
IoT is capable to provide service supportsfor up to mil-
lions of devices due to the add-on value of IoT. Figure6
depicts IoT and industrial services structure with their
4 Cloud innovations inadditive
Manufacturers are looking for a more agile methodology
for system innovation to process support via the cloud.
Cloud technology with adaptable solutions is aligned with
the most advantages of digital manufacturingto meet cus-
tomer demand. Considering novel and existing solutions,
manufacturers need to focus on resource optimization and
cloud-driven capabilities adaption.
Fig. 6 IoT-based cloud and
industrial services structure
1469The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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Cloud enables manufacturing functions to unified use
of intelligent technologies such as:
Digital twins
Mixed reality
Machine learning
Next-generation enterprise resource pPlanning (ERP)
AM is allied with short lead times and due to this AM
has a large degree of flexibility. AM offers multiple advan-
tages such combining multiple parts via design flexibility,
implementation of design elements such as lattice struc-
tures to reduce weight and enhance functionality which is
not possible with conventional methods; usage of compo-
sitionally, structurally, and functionally graded parts; and
manufacturing on site/remote locations thus minimizing
supply chain and transportation related costs. Moreover,
additive manufacturing has opened doors where instead
of selling the parts, the part print files are provided for
purchase and the customer is able to print these files on a
given additive equipment. AM with its benefits is identi-
fied as the ideal technology to be integrated with CM.
Integrated AM/3D printing with CM enables the manufac-
turing process to be more flexible and sustainable.
Figure7 indicates CM architecture. This shows the CM
taxonomy with the relationship and connection of CM con-
ceptions. The Cyber-Physical category includes security
and hybrid manufacturing. Information technology subdi-
vision covers IoT and CC. AM/3D printing is associated
with component design complexity, accuracy, and reliabil-
ity. Manufacturing service-oriented activities are related to
design, testing, and fabrication/production. Resource-shared
services include scheduling and designing. Modeling cat-
egory embraces simulation and optimization. The Agile
manufacturing category involves customer requirements,
fast and flexible operations.
Cloud-based manufacturing combined with other pre-
liminary technologies such as next-generation wireless
capability, advanced sensors, computer-aided design, and
manufacturing software (e.g., CAD, CAM, CAE) represent
smart manufacturing revolution [1]. CM immensely impacts
modern manufacturing through facilitated integration of
extensive supply chains or data streamlined from IoT-based
production equipment. Furthermore, cloud as a digital ser-
vice accounts for about 25 percent of the total involvements
that go into complete manufactured products [1]. Cloud-
based AM systems can move up or down to manage ever-
changing project workloads and propose novel solutions to
meet the manufacturing requirement.
4.1 Cloud‑enabled automation
One of the main objectives of the integrated CM is pro-
cess automation. The manufacturing process will become
further automated through advanced and smart sensors
that allow CM to develop intelligent manufacturing and
designing models in the industry [14]. CM can accom-
plish a real-time analysis of manufacturing and get market
data to design and optimize the manufacturing process
intelligently. This results in decreasing the manufactur-
ing costs and minimizing energy consumption; moreo-
ver, this leads to reducing material waste and downtime.
Manufacturing robots with resource limitations can run
considerable computations in a cloud to improve com-
putational effectiveness [54]. CM automation can over-
come the available limited local information, which can
Fig. 7 CM architecture illustrating its various subcomponents
1470 The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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only access the resources found by the local sensors. By
cloud, manufacturing can operate the flexible and adapt-
able demand signals within a global cloud infrastructure
[54, 55].
Knowing about smart manufacturing and Industry
4.0 [56], the researchers and manufacturers have been
continuously targeting to push the manufacturing toward
challenging directions, such as the fastest service time,
highest equality, lowest cost, streamlined, and maximum
flexibility [57]. CM is a full-scale sharing manufacturing
model that is a computing developed version of the exist-
ing advanced manufacturing model. This model helps to
facilitate data exchanging between different manufac-
turing actions such as product design and process plan-
ning [58]. Full sharing, data transmission, prominent
operations utilization, support for accurate engineering
decisions on time, and use of numerous manufacturing
resources are considered as a top set of CM goals [4951,
4.2 Advanced integration application
formanufacturing process resilience
Manufacturing process systems are vulnerable to genera-
tion of faults due to variations in process composition and
production conditions. CM using advanced real-time data
analysis techniques provides better intelligence, production
process understanding to manufacturers, and technology for
distributed users [62]. Advanced and developed IIoT sensors
help CM in providing a greater quantity and granularity of
data. CM drives on a multi-tenancy cloud platform which
as a knowledge resource center can crowdsource capabil-
ity, capacity, proficiency, and information to increase qual-
ity in the manufacturing system and support the innovative
sustainable manufacturing [14]. The architecture of the
cloud-based AM operations platform with three manufac-
turing layers is shown in Fig.8. Manufacturing service layer
starts to pull data such as operation features and machine
parameters from DCS, SCADA, and sensors robustly and
securely. This data can be transferred through the cloud to
be processed within CM. Then the aggregated data can be
transferred into the manufacturing control layer for machine
utilization, monitoring, scheduling, time adjustment, idling,
and response to a potential failure. Finally, the analyzed data
after processing will be transferred to the user and service
AM is attracting many industries such as aerospace,
automotive, medical, and energy; because of this, accu-
racy and speed are critical aspects. New technologies like
state-of-the-art CM technology bring advances and benefits
over the existing manufacturing processes, specifically AM
technology. These advances and benefits can help to make
maximum material savings, faster speed, accuracy, and
product adjustment with no requirement of machine tooling
for fabrication purposes. Cloud key characteristics include
service sharing, resource scheduling, cloud security, high
speed, and flexibility to facilitate AM process security and
prevent failures.
AM/3D printing has attracted attention in the CM frame-
work; in this regard, different platforms and architectures
have been studied and proposed. 3D printing, produc-
tion, and big data are new emerging topics that receive
Fig. 8 Cloud-based additive
manufacturing operations
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Table 4 Additive manufacturing case studies focused on cloud manufacturing arising from literature to date
Main scope Evaluation Reference
● Using CM-based platform for micro-AM applications ● Constant-time experiment implementation [5]
● Summarizing the characteristics of a new manufacturing paradigm based on
data-enhanced cloud-based designing and manufacturing
● Exploring state of the art in gathering and evaluating product usage and life cycle
data, AM, and sensor integration
● Characterizing a cloud-based design manufacturing model ● Presenting an idealized scenario for design and manufacturing [4]
● Showing a cloud-based AM service system to provide access to a large amount of
CM services
● Presenting a case study to show the cloud AM service application [63]
● Considering manufacturing operations integration in distributed manufacturing
● Proposing an enabler idea of AM
● Studying different CM solutions for AM systems
● Considering a case study to evaluate CM capabilities on the requirements of AM in
the global paradigm
● Introducing a 4-layer AM virtualization system
● Practical study of 3D process technology
● Using the interface and task assignment
● Showing experiments to assess the efficiency and stability of the system
● Proposing a 3D printing platform with CM service for customized production ● Implementation application tools and initial practices
● Analyzing 3D printing online service integration
● Analyzing 3D model library construction
Presenting two cloud models:
● Primary 3D printing cloud
● Advanced 3D printing cloud
● Analyzing 3D printing service paradigms [65]
● Reviewing topics of CM and 3D printing services ● Presenting concepts, techniques, methods, and terminology and performing an
explorative extension study on the domain of AM, CM, 3D printing
● Introducing an application programming interface for 3D printing ● No case studies
● Exposing resources for utilization services within cloud
● Triggering communication structure
● Presenting a cloud-based platform for integration and automation of the order
processing for additively manufactured parts
● Illustrating a showcase and implementation includes Web-based services and
integrated analysis of geometry data for checking manufacturability
● Examination and evaluation of the efficiency and effectiveness of the proposed
● Presenting a 3D printing application model of CM based on 3D printing
● No case studies
● Providing 3D printing application form in CM with three applications
● Proving feasibility analysis of 3D printing in CM
● Proposing an intelligent AM production system for a cloud-based AM space ● Using a case study to modify a vision-based method and apply it to nest the parts in
printing spaces
● Considering an IoT-enabled cloud-based AM platform ● Examination of the current development on online AM services with identification of
three major issues
● Providing a CM platform including different modules such as service modelling,
dynamic matching, intelligent resource distribution optimization, and decision support
● Case study for industrial resource efficiency improvement [3]
● Proposing a game theory-based approach for service scheduling in CM ● An industrial case study for simulation and efficiency evaluation [68]
● Developing a task scheduling optimization for distributed 3D printing services in CM ● Experimental simulations for task changes evaluation [69]
● Proposing a collaborative cloud platform for optimal production ● Case study for evaluation the resources utilization and reducing energy consumption [67]
1472 The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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much attention in CM research or approaches from other
research fields such as IIoT and Industry 4.0. The investi-
gated articles in AM case studies focused on the CM cat-
egory are summarized in Table4. In this table, columns for
authors and year, main idea, and evaluation techniques are
There are a few recent studies/discussions related to intel-
ligent CM and potential benefits[7379] in AM, this topic
attracted the researcher’s attention recently. Most of the
reported efforts in the literature focused on the service-based
category of a cloud or cloud manufacturing application for
3D printers. The reviewed literature mainly can be classified
into two different categories:
Service-driven: providing models/platforms to increase
the services allocation or service scheduling
Quality-driven: providing models to increase 3D printing
quality or resource efficiencies
Considering the lack of a comprehensive solution includ-
ing data sharing, security, technologies integration in AM,
materials flow model, and integration of the existing sys-
tems within a CM system, an intelligent data-driven and
network-driven cloud model is proposed. The proposed
model can support sensor data collection, computation and
communication optimization, and manufacturing network
reliability in AM to process a large amount of data securely
Table 5 Key aspects of CM for
advanced integration in AM CM aspects Application
Process resilience ● Providing better process and design quality
● IIoT sensors help CM with a greater quantity of data
Flexibility and scalability ● Real-time observing and responding
Scheduling ● Selection, evaluation, and composition
● Controlling and optimization
● On-demand manufacturing services
Big data[80, 88] ● Huge amount of data about providers
● Resources data on shop floors
● Cloud services data
● Transaction data
● Consumers and their orders data
● Logistics data
Security ● Data consistency
● Platform security
● Users’ security
● Large scale adoption
Automation and digitalization ● Data layer integration
● Facilitating the manufacturing process to be
automated by the advanced sensors
● Helping CM to develop intelligent models
● Supporting more sustainable manufacturing
solutions implementation
Dynamic monitoring and control ● Dynamic collaborative process monitoring
● System fault detection
● Dynamic resource monitoring
Decentralization ● Coordination of multiple manufacturer
● Innovative sustainable design
● Operates on a multitenancy cloud platform
Table 6 CM impacts on AM
before and after integration Characteristic Before integration After integration
User advantage ● Low ● High
Decision making ● Centralized ● Decentralized
Memory storage ● Limited memory storage ● Accessible storage and other resources
Support services ● Limited support ● Accessible distributed support
Distribution ● Low ● High
● Failure in system is possible ● Failure in system is not possible
Security ● Vulnerable about cyberattacks
and denial of service (DoS)
● Secure against cyberattacks and DoS
Stability ● Stability and agility issues ● Non-stability and agility issues
1473The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
1 3
and efficiently. A “cloud-based solution for smart AM” helps
evaluate scientific aspects of the current methodologies,
which are considered critical for advanced integration for
the production process and AM enhancement.
4.3 A distributed game‑based cloud manufacturing
inadditive manufacturing
AM has several challenges include financial, design, IT inte-
gration, technical, capability challenges. Due to this, CM
integration with AM can be helpful to address those chal-
lenges more efficiently. The integration can enable collabo-
ration for innovative designs and eases access to quantitative
manufacturing processes data. Table5 categorizes the key
aspects of CM and their applications for advanced inte-
gration in AM. Varied cloud manufacturing environments
resulting from multiple cloud services and applications in
the different platforms make some challenges for AM inte-
gration with CM. Some critical aspects may cause those
challenges for integration. Services and components inte-
gration in CM is one of those aspects, or interoperability is
the other one. To integrate AM/3D printing with CM and
support the advanced integrated model, this paper proposes
a 3D printing cloud model and discusses the components.
The proposed cloud-based AM model forms an outline for
the advancement of various AM/3D printing clouds. The
Fig. 9 Dynamic game model
for task and service matching in
AM/3D printing
1474 The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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proposed model can facilitate AM process by data collec-
tion and data analysis accelerated by sensor integration and
automated design process. Table6 compares the impacts of
CM in AM before and after integration.
Recently, game theory applications in cloud manufactur-
ing attracted many researchers to focus on this topic. Game
theory is an effective solution to solve the conflicts and
complexities in real-world problems such as management
of scheduling in manufacturing systems [68, 72, 89]. Liu
etal. [68] developed a game theory-based model for resource
sharing in CM that proposed a model that revealed satisfac-
tion raising for service providers and receivers.
AM tasks have dynamic nature; therefore, they need to be
matched with services accordingly.
The key question being addressed is to appropriately
match AM/3D printing service with a 3D model file[82]
and provide a service for a service receiver. 3D services
can be matched through the game-based model. Here, tasks
customization and 3D printing services are the main points.
The main 3D tasks include model size, model file, accuracy,
materials, location, completion time, and cost. 3D services
include precision, speed, size ability, materials availability,
idle time, and reliability.
Game theory by using a dynamic game structure can pro-
vide task scheduling in AM dynamically [69, 70]. The game
model uses game strategies to choose tasks and match ser-
vices efficiently. Game theory is the interaction of decision-
makers, while control systems involve the design of intelli-
gent decision-making tools [71]. Game theory helps to reveal
the results through interconnected control systems.
AM with its distinctive capabilities provides a novel
unique way of accelerating product advancement.
Data-driven CM paradigm allows AM to efficiently access
various distributed AM resources. Integrating an intelli-
gent cloud with AM can offer high-quality solutions for
3D printing theenvironment in which computation speed
is significantly fast and secure. In this way, CM would real-
time monitor the datasets generated at various stages of
the additive manufacturing process. The initial process
containing pilot print builds of geometrical shapes would
involve common factors with the final part such as the same
print file architecture; similar material; process parameters
including energy input, layer thickness, feedstock charac-
teristics, print file, and processing atmosphere; the same set
of onboard control aware sensors during the manufacturing
which would collect the data and feed it to the control sys-
tem; and identical postmanufacturing protocols involving
non-destructive as well as destructive techniques. Figure10
describes industrial IoT data processing via the cloud in-
situ control-aware sensor in AM. This sensor considers a
dynamic matching and data-driven linkage in AM using a
game-based control strategy. Game theory is a decision-
making approach that plays one side of a game to change
the strategy (3D printing machines) according to a game
player’s decision. The game-based method will be oper-
ated in CM and will help to make a reliable, accurate, and
time-effective production system. CM effectiveness relies
on the data quality and game changes (3D printed products)
used for decision making. Based on the cloud infrastructure
and distributed AM nature, a dynamic game-based data-
driven linkage can support handling AM reliability.
Figure9 represents a dynamic and game theory-based model
in CM for task and service matching in AM/3D printing
systems. Based on the 3D specifications, tasks and services
Fig. 10 Industrial IoT data
processing enabled with insitu
control-aware sensors in AM
illustrated with an example of
laser directed energy deposition
1475The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
1 3
matching are critical and includes the main services such
as size and dimensions, moving speed of nozzle, precision,
and materials. Scheduling operations will occur after ser-
vices matching.
Collected insitu sensors data transfer to cloud supervisory
and data acquisition, then transmits to control the system and
after that, the transmitted data move into the intelligent cloud
for monitoring. A game theory data-driven approach leads to
support collective control to improve data quality in AM sys-
tems. The proposed solution is required for the management of
the distributed sensor networks to address the AM data quality
problem (Fig.10).
5 Conclusion andfuture work
Recently, manufacturing systems have been experienc-
ing challenges due to global technological issues. It is
believed that CM can be a great approach to overcome the
challenges associated with the traditional manufacturing
and expeditiously transition to the on-demand and reli-
able manufacturing capabilities. With the advent of AM
technologies, the need for implementation of CM is ever
increasing. AM technologies integrate multiple steps of
manufacturing into one machine with a coherent set of
steps to produce a finished/near net product. Therefore,
AM is inherently suitable to be integrated with a cloud-
based system. CM embedded cloud game-based control
AM technologies enable features such as sensor monitor-
ing, remote data collection, and intelligent machine inter-
connectivity, thus providing tremendous potential for a
standard pattern to shift across all manufacturing sectors.
A game-based control model in AM can facilitate switch-
ing manufacturing technologies in the CM environment.
Today, the emerging adaptation of the industrial IoT-based
platforms in an advanced manufacturing system can help
leverage the existing legacy manufacturing infrastructures
to potentially transform into a value-added and talented
infrastructure in the era of big data. However, there exist
some difficulties and confusion in the IoT management of
legacy machinery and applicability of the IoT infrastruc-
tures. In fact, IoT and IIoT are all about the continuity of
integrating the past with the future considering short-term
and long-term goals in an extremely competitive global
market of ever-advancing technologies. Advanced addi-
tive manufacturing systems need to maintain competitive
advantages through continuous investments by consider-
ing uncertain time and cost constraints to target a degree
of survivability. As a future direction, the research team
aims to focus on IoT sensors as a new growing demand
to provide more flexibility in AM with stable wireless
Funding The infrastructure and support for this work was provided by
Center for Agile & Adaptive and Additive Manufacturing (CAAAM)
funded through State of Texas Appropriation #190405–105-805008–
220 at the University of North Texas.
Availability of data and material Not applicable.
Code availability Not applicable.
Ethics approval Not applicable.
Consent to participate Not applicable.
Consent for publication Not applicable.
Conflict of interest The authors declare no competing interests.
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... I4.0, also called smart factory, aims to increase factory productivity and efficient resource utilization in real time [17]. The authors [18][19][20] agree that three dimensions essentially outline the I4.0 paradigm: (1) horizontal integration through the value creation network based on intelligent interconnection between companies and the digitization of modules value creation throughout the life cycle of a product and its accessories, (2) engineering throughout the product life cycle through intelligent interconnection and digitalization in all phases, and (3) vertical integration through interconnected manufacturing and logistics systems [21]. For Szabó-Szentgróti et al. [22], I4.0 should have six fundamental principles: virtualization, interoperability, decentralization, real-time capability, service orientation, and modularity. ...
... Number of publications per subgroup: A total of four subgroups were used in the literature review. The IoT subgroup made the largest contribution (with 27 studies), followed by CPS (20), CC (12), and AM (8). Nineteen methodological proposals cover all subgroups, and eight proposals did not consider any subgroup but instead cover methodologies for evaluating and analyzing the challenge of the lack of adequate skills to accelerate the progress towards the fourth industrial revolution ( Figure 5). ...
... Number of publications per subgroup: A total of four subgroups were use literature review. The IoT subgroup made the largest contribution (with 27 stud lowed by CPS (20), CC (12), and AM (8). Nineteen methodological proposals c subgroups, and eight proposals did not consider any subgroup but instead cov odologies for evaluating and analyzing the challenge of the lack of adequate ski celerate the progress towards the fourth industrial revolution ( Figure 5). ...
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In this work, we integrate the concepts of Industry 4.0, smart manufacturing, and sustainable manufacturing in a model that provides a conceptual framework for the study of long-term solutions with a high degree of specialization, according to the specific context of each investigation. This study offers a holistic analysis and evaluation of the main challenges facing the Industry 4.0 concept. We also diagnose the current methodological proposals aimed at solving the challenges of Industry 4.0 and sustainability using a systemic review of the literature from the past 5 years. Firstly, we identify 14 technological trends linked to Industry 4.0. Subsequently, the trends are integrated into the proposed model to identify opportunities, evaluating their relationship with three performance areas. This allows the identification of trends that present the greatest number of opportunities in the context of sustainability. The second stage complements the literature review with a descriptive analysis of the studies and discusses the findings. The study concludes that the identified technological trends positively impact Industry 4.0 challenges, helping to achieve sustainable manufacturing objectives.
... Send it to the monitoring center through RS3232 serial port. Doctors and nurses on duty can view the physiological parameters of patients at any time through the monitoring management center [14]. They cannot only find the possible emergencies of critical patients in time, but also provide the best suggestions and means of health treatment for ordinary patients. ...
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The postoperative results of cerebrovascular surgery patients have been successfully used in medical practice using the Internet. The results obtained through data analysis were used in the study. So far, 120 patients who underwent cerebrovascular surgery from February 2018 to December 2018 have been enrolled. The selected class was divided into two groups: 60 psychiatric patients, a control group and an observation group. The former is medical treatment and the latter is postoperative treatment. Results: The results showed that the blood pressure of control group was lower than that of control group, and the incidence of adverse events was lower than that of control group ( P < 0.05 ). Meanwhile, the average hospitalization rate of cerebrovascular disease patients in control group was lower than that in control group ( P < 0.05 ). Conclusion: For patients with cerebrovascular disease, postoperative nursing can reduce the incidence of postoperative complications, reduce the risk of surgery, and improve the effect of surgery. Acute ischemic stroke refers to a kind of clinical syndrome caused by abnormal blood supply in the brain, resulting in ischemia, hypoxic brain tissue necrosis, and focal or comprehensive neurological deficiency. Among them, progressive cerebral infarction accounted for about 20~35%, and most occurred in the early stage of the disease (48~72)h.
... Using the indexed keywords for network diagrams is because the author's keywords result in duplicity of To manufacture the other type of screws as well apart from the two mentioned The different biocompatible and biodegradable materials to be explored for printing screws and metallic implants for orthopaedic applications using AM techniques 15 Haghnegahdar et al. [61], 2021 Intelligent AM using IoT-based cloud manufac- ...
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The current study skims the trends, opportunities, and challenges in integrating additive manufacturing with Industry 4.0. The critical points of the existing review studies have also been discussed. The search query related to AM and Industry 4.0 was used for obtaining the information from two databases: Web of Science and Scopus. The papers were screened for duplicity and irrelevancy according to the topic of study. The bibliometric software R studio, Hiscite and Vosviewer were used for the analysis of downloaded articles. The bibliometric information related to the most-cited and productive authors, countries, sources, and universities was extracted. Lotka’s and Bradford’s law applicability to authors and sources, respectively, have been demonstrated. The interconnections between the authors, their respective countries and universities were represented with the help of three-field plot. The trend topics, keywords, and thematic evolution form the basis of a review of the cited work. The critical issues related to AM for achieving Industry 4.0 were reviewed. The insight of the case studies powering industry 4.0 was also presented. The challenges and limitations of AM’s implementation with respect to Industry 4.0 were highlighted. The conclusions were drawn out, and future scope was pointed out.
... Considerable efforts have been made to explore the feasibility of utilizing emerging information technologies, such as cloud computing [19], data analytics [20] ,and artificial intelligence (AI) [21,22] to solve specific AM problems. Meanwhile, initiatives for intelligent AM, also known as smart AM, have been made in references [23][24][25]. The development of this fast-moving field calls for a formal concept of IAMD, and there is currently no clear roadmap for guiding its future development. ...
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In additive manufacturing (AM), intelligent technologies are proving to be a powerful tool for facilitating economic, efficient, and effective decision-making within the product and service development. Such capabilities hold great promise to significantly improve the producibility, repeatability, and reproducibility of the additive manufacturing process and unlock its complete design freedom for product innovation. This paper defines the concept of intelligent additive manufacturing and design (IAMD) while providing a triple-layer model for reference. Details about these three layers, i.e., digital thread layer, cyber-physical layer, and intelligent service layer, are presented. Moreover, both scientific and engineering challenges raised during the studies and implementations of IAMD are discussed together with potential solutions. The paper also outlines the future perspective on IAMD towards the directions of integrated design and manufacturing, cyber-physical AM, advanced artificial intelligence for AM, digital materials and products, as well as design for AM process chain.
... Provide reliability data support for DMS Provide reliability analysis method/tool support Complex product quality reliability Manufacturing process buffer management Research on intelligent manufacturing process and reliability application based on real-time reliability data-driven and Machine-Buffer-Quality-Reliability model The development of the Industrial Internet of Things (IIoT) enables intelligent manufacturing processes and intelligent control of complex products. Cloud-based remote data collection, intelligent machine interconnection and sensor monitoring technologies provide a new direction for the optimization of intelligent manufacturing processes based on machine states [55]. In order to better evaluate the technical condition of machine operation, various possible machine state signals, such as failure information of key components, such as bearings, equipment vibration signals and pressure signals, are obtained through high-precision sensors, IIoT, etc. From these feature signals, feature vectors are extracted and counted to identify the machine operation status. ...
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With the application of new-generation information technology in the full life cycle process of a complex product, it is showing the characteristics of multi-source, real-time, heterogeneous, cross-domain transmission. Large data volume and low value density emerge in the process of complex product design manufacturing and services (DMS). This leads to “information islands” and insufficient utilization of cross-domain reliability data in the process of integration of DMS for complex product R&D design data, manufacturing data and operation and maintenance services (O&MS) data. This paper proposes and illustrates a framework of complex product DMS integration based on reliability data, including complex product design optimization based on manufacturing and service reliability data, complex product intelligent manufacturing process optimization based on real-time reliability data and complex product O&MS optimization based on multi-source heterogeneous reliability data. Additionally, it then realizes complex product design reliability and optimization, manufacturing process reliability and optimization and O&MS reliability and intelligent decision optimization based on reliability data. Finally, the DMS integration framework based on reliability-data-driven proposal is corrected through the case of engine MDS integration, which can effectively improve the cross-domain reliability data utilization and overall product reliability of complex products. The proposed framework extends the application of reliability theory in the process of complex product DMS integration and provides a reference for enterprises in the R&D, manufacturing and O&MS of complex products.
... The implementation of the new customer management system can support inner management that serves business needs. Such business processes that implement in old ways are not comprehensive and not able to drive new goals [14]. It addresses the incomplete data resulting in customer feedback information not accurate and misleading. ...
Conference Paper
Service Advisor in Automotive Service Centre plays an important role as the frontline in providing exceptional services. The automotive service centre has to adopt big data applications in understanding customers’ needs by collecting data promptly and analysing scientifically. The objective of this paper is to evaluate Customer Satisfaction (CS) and Service Advisor Experience (SAE) scores via an online survey based on big data analytics. Thus, applying a Quadrifid graph in identifying focus regions for improvement activities. The application of big data online survey platforms is an efficient way of gathering customer feedback for continuous improvement activities. The study focused on Service Advisor (SA) services throughout Malaysia with selected one automotive brand. It explains the definition of customer process and customer satisfaction by comparing high-density customer regions namely Central, Northern and Southern regions with low-density customer regions namely East Coast and East Malaysia regions. There are five steps in deriving the output, which are the consolidation of customer data, customer selection, survey execution, score calculation and analytical report. Thus, the big data applications analyse the expectation SA gap and propose recommendation actions. The online survey results achieved a minimum of 879.90 points for Customer Satisfaction while Service Advisor Experience was minimum at 73%. SA achieved a high score for portraying courtesy and professionalism, while a lack of performing the visual inspection is the main gap for all regions. Detailed analysis using Quadrifid graph interpreted Southern region recorded the lowest correlation with R-square value less than 0.1 and level of CS & SAE below the average value of 800 relates to response towards needs by SA. In this paper, the outcome of the execution is centralization of customer information, Service Level Agreement standard, customer handling norms and work efficiency improvement. Such indicators lead to the SA's professionalism in managing customer expectations.
... e application of IIoT technology to Baijiu brewing will help enterprises digitally transform and improve enterprise production efficiency and will also create new opportunities to meet the needs of differentiated enterprises [5,6]. Embedding IIoT technology in the manufacturing industry has significantly increased the level of digitalization of enterprises and shortened production cycles [7]. ...
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In order to explore the segmented relationship between the segmented rating results of Baijiu and the segmented characteristics of alcohol content and cumulative flow in the distillation process and to verify whether different liquor categories can achieve rating through the segmented characteristics of alcohol content and cumulative flow, in view of this, combined with the Industrial Internet of Things (IIoT) technology and online detection and analysis of alcohol and cumulative traffic, its accuracy can reach ±0.5%, ±1%, with the integration of Baijiu categories, sources, liquor characteristic conditions, and other multisource data, to achieve Baijiu segmented rating data reconstruction, the use of standard error and standard deviation as evaluation indicators, quantification of distilled liquor alcohol, and cumulative flow segmented characteristics to form a liquor rating strategy, so as to use the Arduino platform control motor to achieve automatic grading of Baijiu. Experiments show that the relative error between automatic rating and manual rating is less than 10%, which shows that automatic rating can be better applied to the actual brewing process. It provides a solution for the digitization and standardization of Baijiu grading.
... Sun et al., 2010;Sharma et al., 2020;Desingh, 2021;Caiado et al., 2022;Wu et al., 2022;Farooq et al., 2015;Sharma et al., 2020;Riggins & Wamba, 2015;Kamble et al., 2019;Haghnegahdar et al., 2022;Wang et al., 2022;Xing et al., 2021;Umar et al., 2021a) System failure issue/integrity ...
Many industrial firms have gained strategic benefits from the use of IoT in supply chain (SC) and operations, but still, several firms are reluctant in applying this technology in their firm. Therefore, this study is going to identify and analyze the most influential barriers faced by firms during the adoption of IoT in attaining sustainable SC. The novelty of the current research work is the use of Analytical Hierarchy Process (AHP) under spherical fuzzy set to assess those identified barriers which are linked with IoT implementation in the context of sustainable SC. Compared to previously developed extension of fuzzy sets such as Intuitionistic and Pythagorean fuzzy sets, Spherical fuzzy set uses three dimensional membership functions capable of handling more uncertainty and ambiguity of preference given by the decision makers. The barriers are analyzed by segregating them into four major categories i.e. economic, organisational, environmental, and technological barriers. In terms of computational complexity the proposed spherical fuzzy AHP is much simpler to the previously developed spherical fuzzy AHP, as it utilizes the newly proposed Spherical Fuzzy Geometric Mean to compute the final score of the barriers. It is found that economic factor is the most influential factor among organizational, environmental and technological barriers. Overall operational cost and financial constraints/insufficient budget are the two most influential factors or major cause which acts as a hurdle in implementing IoT in the context of sustainable SC. The analysis on the crucial barriers in the adoption of IoT in sustainable SC suggest that financial rewards and compensations should be provided to the firms who adopted green material and green devices in IoT technologies.
... The fourth industrial revolution (Industry 4.0) is a paradigm of the cyber-physical world whose philosophy is based on fully automated and digitalized smart factories for enhanced production and customized user experience [1][2][3]. Various key enabling technologies are involved in one way or the other to bring the concept of Industry 4.0 to operation, such as cloud computing [4,5], big data [6], Industrial Internet of Things (IIoT) [4,5,7], digital twins [8], artificial intelligence [9][10][11][12][13], smart communication [14][15][16][17], additive manufacturing [18,19], advanced robotics [20][21][22], and cyber-physical systems [7,8]. The main objective of Industry 4.0 revolves around automation and mass-productivity without much human intervention. ...
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Industry 4.0 is a new paradigm of digitalization and automation that demands high data rates and real-time ultra-reliable agile communication. Industrial communication at sub-6 GHz industrial, scientific, and medical (ISM) bands has some serious impediments, such as interference, spectral congestion, and limited bandwidth. These limitations hinder the high throughput and reliability requirements of modern industrial applications and mission-critical scenarios. In this paper, we critically assess the potential of the 60 GHz millimeter-wave (mmWave) ISM band as an enabler for ultra-reliable low-latency communication (URLLC) in smart manufacturing, smart factories, and mission-critical operations in Industry 4.0 and beyond. A holistic overview of 60 GHz wireless standards and key performance indicators are discussed. Then the review of 60 GHz smart antenna systems facilitating agile communication for Industry 4.0 and beyond is presented. We envisage that the use of 60 GHz communication and smart antenna systems are crucial for modern industrial communication so that URLLC in Industry 4.0 and beyond could soar to its full potential.
Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research.
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Cloud computing technology has been studied in the context of industry 4.0 as a tool applied to manufacturing services and resources. Such concept is widely known as Cloud Manufacturing. This paper aims at mapping the current state of academic researches on this field, promoting the understanding of trends, references and practical applications in real-life conditions. A bibliometric analysis was conducted using two different databases – Scopus and Web of Sciences – and VOSviewer’s text mining tools and techniques. From a sample of 1,420 papers, this study identified the countries which had the largest volume of publications, the main journals related to the subject, the most influent articles, and four clusters by keywords occurrences: (i) “Optimization of manufacturing processes”, (ii) “Collaborative networks of manufacturing resources and services”, (iii) “Industry 4.0 and cloud computing systems”, and (iv) “Data reliability and cyber-security”. Finally, this work selected and analyzed the 159 articles with applied case studies, in order to stratify and to understand the most common approaches within the four pre-established categories. This article can contribute to researchers and developers searching for successful practical applications in digitalization of manufacturing chains, as well as to those who are looking for gaps in the still unexplored fields of Cloud Manufacturing. Both the assessment and the categorization of the case studies about Cloud Manufacturing are the differentials in this article.
Full-text available
Cloud computing technology has been studied in the context of industry 4.0 as a tool applied to manufacturing services and resources. Such concept is widely known as Cloud Manufacturing. This paper aims at mapping the current state of academic researches on this field, promoting the understanding of trends, references and practical applications in real-life conditions. A bibliometric analysis was conducted using two different databases – Scopus and Web of Sciences – and VOSviewer’s text mining tools and techniques. From a sample of 1,420 papers, this study identified the countries which had the largest volume of publications, the main journals related to the subject, the most influent articles, and four clusters by keywords occurrences: (i) “Optimization of manufacturing processes”, (ii) “Collaborative networks of manufacturing resources and services”, (iii) “Industry 4.0 and cloud computing systems”, and (iv) “Data reliability and cyber-security”. Finally, this work selected and analyzed the 159 articles with applied case studies, in order to stratify and to understand the most common approaches within the four pre-established categories. This article can contribute to researchers and developers searching for successful practical applications in digitalization of manufacturing chains, as well as to those who are looking for gaps in the still unexplored fields of Cloud Manufacturing. Both the assessment and the categorization of the case studies about Cloud Manufacturing is a differential in this article.
Full-text available
This book introduces a cross-layer design to achieve security and resilience for CPSs (Cyber-Physical Systems). The authors interconnect various technical tools and methods to capture the different properties between cyber and physical layers. Part II of this book bridges the gap between cryptography and control-theoretic tools. It develops a bespoke crypto-control framework to address security and resiliency in control and estimation problems where the outsourcing of computations is possible. Part III of this book bridges the gap between game theory and control theory and develops interdependent impact-aware security defense strategies and cyber-aware resilient control strategies. With the rapid development of smart cities, there is a growing need to integrate the physical systems, ranging from large-scale infrastructures to small embedded systems, with networked communications. The integration of the physical and cyber systems forms Cyber-Physical Systems (CPSs), enabling the use of digital information and control technologies to improve the monitoring, operation, and planning of the systems. Despite these advantages, they are vulnerable to cyber-physical attacks, which aim to damage the physical layer through the cyber network. This book also uses case studies from autonomous systems, communication-based train control systems, cyber manufacturing, and robotic systems to illustrate the proposed methodologies. These case studies aim to motivate readers to adopt a cross-layer system perspective toward security and resilience issues of large and complex systems and develop domain-specific solutions to address CPS challenges. A comprehensive suite of solutions to a broad range of technical challenges in secure and resilient control systems are described in this book (many of the findings in this book are useful to anyone working in cybersecurity). Researchers, professors, and advanced-level students working in computer science and engineering will find this book useful as a reference or secondary text. Industry professionals and military workers interested in cybersecurity will also want to purchase this book.
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Cloud manufacturing represents a valuable tool to enable wide sharing of manufacturing services and solutions by connecting suppliers and customers in large-scale manufacturing networks through a cloud platform. In this context, with increasing manufacturing network size at global scale, the elevated number of manufacturing solutions offered via cloud platform to connected customers can increase the complexity of decision-making, resulting in poor user experience from a customer perspective. To tackle this issue, in this paper, an intelligent decision-making support tool based on a manufacturing service recommendation system (RS) is designed and developed to provide for tailored manufacturing solution recommendation to customers in a cloud manufacturing system. A machine learning procedure based on neural networks for data regression is employed to process historical data on user manufacturing solution preferences and to carry out the automatic extraction of key features from incoming user instances and compatible manufacturing solutions generated by the cloud platform. In this way, the machine learning procedure is able to perform a customer segmentation and build a recommendation list characterized by a ranking of manufacturing solutions which is tailored to the specific customer profile. With the aim to validate the proposed intelligent decision-making support system, a case study is simulated within the framework of a cloud manufacturing platform delivering dynamic sharing of sheet metal cutting manufacturing solutions. The system capability is discussed in terms of machine learning performance as well as industrial applicability and user selection likelihood.
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Recent advancements in Industry 4.0 key enabling technologies allow for the dynamic sharing of additive manufacturing services in a cloud manufacturing context, with great potential on resource efficiency improvement at network level. This paper proposes the conceptualization and development of a modular-structured cloud platform to match users’ instances generating feasible solutions according to various manufacturing scenarios. The proposed cloud manufacturing platform includes a service modelling and dynamic matching module, an intelligent resource distribution optimization module and a decision-making support module to assist users in characterizing the generated solutions. A simulated case study is reported to exemplify technical and economic advantages for industrial resource efficiency improvement.
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Cloud manufacturing is emerging as a new manufacturing paradigm and an integrated technology. To adapt to the increasing challenges of the traditional manufacturing industry transforming toward service-oriented and innovative manufacturing, this paper proposes a product platform architecture based on cloud manufacturing. Firstly, a framework for the product platform for cloud manufacturing was built. The proposed architecture is composed of five layers: resource, cloud technology, cloud service, application, and user layers. Then, several key enabling technologies for forming the product platform were studied. Finally, the product platform for cloud manufacturing built by a company was taken as an application example to illustrate the architecture and functions of the system. The validity and superiority of the architecture were verified.
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This paper deals with a methodology for the implementation of cloud manufacturing (CM) architecture. CM is a current paradigm in which dynamically scalable and virtualized resources are provided to users as services over the Internet. CM is based on the concept of cloud computing, which is essential in the Industry 4.0 trend. A CM architecture is employed to map users and providers of manufacturing resources. It reduces costs and development time during a product lifecycle. Some providers use different descriptions of their services, so we propose taking advantage of semantic web technologies such as ontologies to tackle this issue. Indeed, robust tools are proposed for mapping providers’ descriptions and user requests to find the most appropriate service. The ontology defines the stages of the product lifecycle as services. It also takes into account the features of cloud computing (storage, computing capacity, etc.). The CM ontology will contribute to intelligent and automated service discovery. The proposed methodology is inspired by the ASDI framework (analysis–specification–design–implementation), which has already been used in the supply chain, healthcare and manufacturing domains. The aim of the new methodology is to propose an easy method of designing a library of components for a CM architecture. An example of the application of this methodology with a simulation model, based on the CloudSim software, is presented. The result can be used to help the industrial decision-makers who want to design CM architectures.
To improve the multi-task scheduling competitivenesss of the distributed three-dimensional (3D) printing services with different types in cloud manufacturing, a non-cooperative game model of 3D printing services is proposed to reduce completion time, cost and to improve service quality. Moreover, the non-cooperative game consists of two kinds of sub-game to work together. Some service attributes, such as moving speed of nozzle, model dimension, 3D printing precision, 3D printing material, and pricing mode, are considered in the model. In order to obtain the expected solution, a two-layer nested method based on genetic algorithm is developed to improve scheduling efficiency. An industrial case is given to verify the feasibility and effectiveness of the proposed method. The results show that it has a better performance than traditional scheduling methods.
As an emerging sharing and collaborative paradigm, the cloud manufacturing system should maximize the satisfaction of stakeholders to promote the long-term development of the system. This article proposes a new utility-aware cloud manufacturing multi-task scheduling model, which considers the utilities of both customers and manufacturers. To solve the proposed model, an extended non-dominated sorting genetic algorithm-II with three improvements is presented to find the approximate optimal Pareto solution set. Then, these non-dominated solutions are ranked by means of game theory, and the resulting optimal solution is recommended to the cloud manufacturing system. Simulation experiments are conducted to verify the effectiveness of the proposed algorithm by comparing it with three baseline multi-objective evolutionary algorithms.
Purpose This paper proposes a model based on minority game (MG) theory to support the decision-making regarding the efficient allocation and exploitation of resources/services among the partners of a cloud manufacturing (CMfg) system. CMfg system is a new manufacturing paradigm to share manufacturing capabilities and resources on a cloud platform. The use of a decision model to organize and manage the resources and services provided by the autonomous participants of a CMfg has crucial relevance for the system's effectiveness and efficiency. Design/methodology/approach This research proposes a noncooperation model based on MG theory. The MG is designed to make decisions on the use of resources/services among the partners of CMfg with private information. A simulation environment was developed to test the efficiency of the proposed decision model. Moreover, an ideal decision model with complete information among the partners was used as a benchmark model. Findings The simulation results show how the application of the proposed MG model outperforms the MG model usually proposed in the literature. In particular, the proposed decision model based on private information has an efficiency closer to the ideal model with complete information among the partners of a CMfg. Originality/value This paper advances knowledge about the application of MG in the field of CMfg system. The proposed decision-making model based on MG is a promising approach to help enterprises, and especially small and medium enterprises, to participate in CMfg initiatives and to develop their business.