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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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https://doi.org/10.1007/s00170-021-08436-x
CRITICAL REVIEW
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
Abstract
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
1 3
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
process.
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
[18].
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
[19].
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
entirely.
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-
turing
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
objective
(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
communication
(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
evaluation
(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
method
(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-
tization
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
[41].
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
communications
Operation’s incident recovery
Data fusion, conversion
Access control for several users
Platform functions customization
Machine
Automation
Transaction
Monitoring
Schedule
Optimization
Situational
Evaluation
Awareness
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)
manufacturing
IoT CM-CC IIoT
Monitoring On-demand computing
services with high reliability
Higher levels of automation and control, i.e., deep
learning
Control Availability Reduces the cognitive loads placed on the operator
Big data and
business
analytics
Scalability Smart functionality that provides feedback system to a
system that is self-controlling
Information
sharing and
collaboration
Working non-stop in a
distributed industrial
environment
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
relations.
4 Cloud innovations inadditive
manufacturing
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
1 3
Cloud enables manufacturing functions to unified use
of intelligent technologies such as:
IoT
Blockchain
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
1 3
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,
5961].
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
requester.
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
platform
1471The International Journal of Advanced Manufacturing Technology (2022) 119:1461–1478
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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
[8]
● 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
networks
● 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
[10]
● 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
[30]
● 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
[64]
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
[11]
● Introducing an application programming interface for 3D printing ● No case studies
● Exposing resources for utilization services within cloud
● Triggering communication structure
[81]
● 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
platform
[2]
● Presenting a 3D printing application model of CM based on 3D printing
characteristics
● No case studies
● Providing 3D printing application form in CM with three applications
● Proving feasibility analysis of 3D printing in CM
[66]
● 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
[13]
● Considering an IoT-enabled cloud-based AM platform ● Examination of the current development on online AM services with identification of
three major issues
[9]
● Providing a CM platform including different modules such as service modelling,
dynamic matching, intelligent resource distribution optimization, and decision support
module
● 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
1 3
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
discussed.
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
1 3
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
process
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
communications.
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.
Declarations
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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