machines

Systematic Review
Promoting Synergies to Improve Manufacturing Efficiency
in Industrial Material Processing: A Systematic Review
of Industry 4.0 and AI
Md Sazol Ahmmed , Sriram Praneeth Isanaka and Frank Liou *

Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology,
Rolla, MO 65409, USA; ma8gf@mst.edu (M.S.A.); sihyd@mst.edu (S.P.I.)
* Correspondence: liou@mst.edu

Abstract: The manufacturing industry continues to suffer from inefficiency, excessively high prices,
and uncertainty over product quality. This statement remains accurate despite the increasing use
of automation and the significant influence of Industry 4.0 and AI on industrial operations. This
review details an extensive analysis of a substantial body of literature on artificial intelligence (AI)
and Industry 4.0 to improve the efficiency of material processing in manufacturing. This document
includes a summary of key information (i.e., various input tools, contributions, and application
domains) on the current production system, as well as an in-depth study of relevant achievements
made thus far. The major areas of attention were adaptive manufacturing, predictive maintenance,
AI-driven process optimization, and quality control. This paper summarizes how Industry 4.0
technologies like Cyber-Physical Systems (CPS), the Internet of Things (IoT), and big data analytics
have been utilized to enhance, supervise, and monitor industrial activities in real-time. These
techniques help to increase the efficiency of material processing in the manufacturing process, based
on empirical research conducted across different industrial sectors. The results indicate that Industry
4.0 and AI both significantly help to raise manufacturing sector efficiency and productivity. The
fourth industrial revolution was formed by AI, technology, industry, and convergence across different
engineering domains. Based on the systematic study, this article critically explores the primary
limitations and identifies potential prospects that are promising for greatly expanding the efficiency
Citation: Ahmmed, M.S.; Isanaka, S.P.;
of smart factories of the future by merging Industry 4.0 and AI technology.
Liou, F. Promoting Synergies to
Improve Manufacturing Efficiency in Keywords: Industry 4.0; artificial intelligence; material processing; smart factory; manufacturing
Industrial Material Processing: A efficiency
Systematic Review of Industry 4.0 and
AI. Machines 2024, 12, 681. https://
doi.org/10.3390/machines12100681
1. Introduction
Academic Editor: António Pereira
Industry 4.0, which is sometimes referred to as the fourth industrial revolution, is a
Received: 20 August 2024 term that describes the transformation of traditional manufacturing processes into a cyber-
Revised: 23 September 2024
physical production system (CPPS) through the utilization of digital technology integrated
Accepted: 25 September 2024
with physical manufacturing. Through the utilization of real-time data monitoring and
Published: 29 September 2024
the promotion of collaborative efforts within the production team, this system can make
assessments and even propose corrective actions [1]. A significant number of small and
medium-sized firms, and even many major corporations, are implementing several of the
Copyright: © 2024 by the authors.
technologies that are part of Industry 4.0 to maintain their position in global competition in
Licensee MDPI, Basel, Switzerland. terms of cost, quality, productivity, and reliability [2]. Companies integrating Industry 4.0
This article is an open access article technologies—the Internet of Things (IoT), cloud computing, and computerized production
distributed under the terms and systems into current facilities are thus turning them into smart factories. The physical
conditions of the Creative Commons systems of a factory are helping us to complete this metamorphosis. Driven by its use of
Attribution (CC BY) license (https:// automation and mechanization, a smart factory is an upgraded form of the conventional
creativecommons.org/licenses/by/ factory model. It can control a broad spectrum of product manufacture and show agility in
4.0/). reaction to demand fluctuations. This may be accomplished by combining the technology

Machines 2024, 12, 681. https://doi.org/10.3390/machines12100681 https://www.mdpi.com/journal/machines

Machines 2024, 12, x FOR PEER REVIEW 2 of 28

Machines 2024, 12, 681 2 of 27

show agility in reaction to demand fluctuations. This may be accomplished by combining

the technology of cyber systems with the manufacture of physical goods [3]. This type of
of cyber systems with the manufacture of physical goods [3]. This type of manufacturing,
manufacturing, which is connected by real-time data monitoring and continuous data
which is connected by real-time data monitoring and continuous data streaming, not only
streaming, not only reduces the amount of time needed for production and rework but
reduces the amount of time needed for production and rework but also enhances the
also enhances the reputation of the company because of its ability to precisely sort mate-
reputation of the company because of its ability to precisely sort materials, anticipate
rials, anticipate machine failure at an early stage, and recognize and evaluate significant
machine failure at an early stage, and recognize and evaluate significant events [4–6]. In
events [4–6]. In addition, the deployment of IoT in industrial systems may assist in the
addition, the deployment of IoT in industrial systems may assist in the early identification
early identification
of problems, of problems,
the flexibility the flexibility
to handle designtomodifications
handle designinmodifications
products, the in detection
products,
thedata
of detection
anomaliesof data anomalies
during during maintenance,
maintenance, and the implementation
and the implementation of various
of various production
production processes [7–9]. In addition, the use of Industry
processes [7–9]. In addition, the use of Industry 4.0 techniques, such as big data analysis 4.0 techniques, such as big
data analysis and cloud computing, contributes to the development
and cloud computing, contributes to the development of a distributed production system of a distributed pro-
duction system in which all data and information are centralized
in which all data and information are centralized and can be employed according to the and can be employed
according to the
requirements requirements
of the customer of the customer
[10,11]. [10,11]. These
These technologies technologies
enable the producerenableto themake
pro-
ducer to based
decisions make on decisions based on
a wide variety a wide
of data variety
sources of data
via the means sources via the means
of the identification of of the
major
identification of major patterns and trends [12]. In addition,
patterns and trends [12]. In addition, the current advancement in the production system the current advancement in
the production system known as additive manufacturing
known as additive manufacturing has made it possible for producers and customers has made it possible for produc-
ershave
to and customers
a more intimate to haverelationship
a more intimate with relationship
one anotherwith one another
through throughofthe
the utilization uti-
these
lization of these contemporary technologies [12,13]. By
contemporary technologies [12,13]. By establishing a common method of communication establishing a common method of
communication
using websites and usingthe websites
internet, and the internet,
additive additive(AM)
manufacturing manufacturing
has the potential (AM) to hasenhance
the po-
tential
the to enhance
efficiency of the thesupply
efficiency chain of the
andsupply chain
facilitate theand facilitate
rapid the rapid
prototyping of prototyping
materials [13]. of
materials
Other new[13]. Other newthat
technologies technologies
are part ofthat are part
Industry 4.0ofcontribute
Industry 4.0 contributeproductivity
to increased to increased
productivity
in this way. The in this way. The combination
combination of lean technologies
of lean technologies with Industry with 4.0Industry
will also4.0 will
make also
it
make it possible to create production systems that are both flexible
possible to create production systems that are both flexible and very cost-effective [14]. and very cost-effective
[14]. Optimizing
Optimizing the processing
the processing of materials
of materials is another is another
significantsignificant
emphasisemphasis
that should thatbeshould
made
beincrease
to made toproduction
increase production
efficiency. Itefficiency.
is possibleItto is enhance
possibleoverall
to enhance overall
efficiency by efficiency
reducing the by
reducingofthe
amount timeamount
spent of ontime spent onimproving
production, production, improving
product product
quality, quality,a and
and saving saving
significant
amount of money.
a significant amount Theofprocessing
money. The of processing
additive materialsof additivecomprises
materials several processes,
comprises all
several
of which ought
processes, all ofto be better
which ought controlled
to be better to achieve
controlled thetogoal that isthe
achieve depicted
goal that in is
Figure 1 [15].
depicted in
Usually, in conjunction with additive processes, the method starts
Figure 1 [15]. Usually, in conjunction with additive processes, the method starts with first with first preparing the
components,
preparing thewherein components,materialswhereinundergo refinement
materials undergo to increase
refinement thetogeneral
increase quality of the
the general
final print.
quality Using
of the finalmethods like powder-based
print. Using or wire-based or
methods like powder-based additive
wire-basedmanufacturing—in
additive man-
the processing phase—the
ufacturing—in the processing material
phase—the is deposited
materialinislayers
depositedto gradually
in layers to form the desired
gradually form
geometry.
the desired Reaching
geometry.the intended
Reaching the object
intended dimensions dependsdepends
object dimensions on precisely controlling
on precisely con-
the material deposition. To guarantee that the component
trolling the material deposition. To guarantee that the component has the necessary has the necessary strength
and durability,
strength postprocessing
and durability, steps like
postprocessing curing,
steps surfacesurface
like curing, finishing and heat
finishing and treatment
heat treat-
are employed. This procedure is crucial to guarantee
ment are employed. This procedure is crucial to guarantee that the resulting part that the resulting part is accurate,
is accu-
long-lasting,
rate, long-lasting,and suitable for use
and suitable for[15].
use [15].

Figure 1.
Figure 1. Material
Material processing
processing steps
steps during
during the
the additive
additive process
processbased
basedon
onthe
thedata
datain
inRef.
Ref.[15].
[15].

Producing aa finished
Producing finished product
product that
that satisfies
satisfies the
the client’s
client’s requirements
requirements includes
includes aa broad
broad
range
range ofof processes
processes such
such as
ascasting,
casting,forming,
forming,machining,
machining, and andfabrication
fabrication[15].
[15]. Applying
Applying
Industry
Industry 4.04.0 technologies
technologies helps
helps us us better
better control
control quality
quality and
and limits
limits the
the expenditure
expenditure on on
such
such processes;
processes; ultimately,
ultimately, itit will
will lead
lead to
to profitability.
profitability. ToTo accomplish
accomplish this
this objective,
objective, the
the
use
use of
of manufacturing
manufacturing processes that make use of data analytics and AI is critical to im-
prove
prove product quality and
and expedite
expedite production.
production.AllAllthetheinput
inputvariables
variables
areare guaranteed
guaranteed to
to be accurate and consistent if a method that is both consistent and rigorous is put into
practice [16]. In addition, the deployment of predictive maintenance and the increased
Machines 2024, 12, 681 3 of 27

utilization of available and pre-deployed resources are two other factors that lead to cost
reductions in the industrial sector [17]. An example of these factors is the reduction in
expenses related to breakdowns, maintenance, rework, and overhauls. Resilience and
flexibility are two qualities that material processing must exhibit if it is to continue to be
competitive in the global market. Thanks to the fourth industrial revolution, we can now
create a manufacturing line that is adjustable and versatile [18,19]. The development of
robots and additive manufacturing has further facilitated collaboration between customers
and manufacturing enterprises. This advancement has enabled the rapid creation of initial
models and large-scale item customization, eliminating the need for costly equipment
modifications.

Role of AI in Enhancing Manufacturing Efficiency

AI is a computer-enabled interactive group of algorithm systems that can execute tasks
using its own intellect, like how a human utilizes their brain to fulfill a task or complete
an action targeting a desirable outcome within the constraints of a problem. The tasks
encompassed by this category [20] comprise the understanding of natural languages, the
identification of patterns, and the assessment of independent and unique circumstances, all
to propose immediate action as necessary. Among the technologies that are included in the
category of AI are machine learning (ML) [21], deep learning [22], computer vision [23],
and natural language processing (NLP) [24]. A few examples of these technologies are
included in the forthcoming sections. The use of these technologies is being pursued by
a wide range of firms to enhance the effectiveness of their internal procedures. A variety
of industries make use of these technologies to improve the effectiveness of systems that
are deployed inside their own businesses. When it comes to improving the efficiency of
production in a variety of industries, AI, just like other types of organizations or busi-
nesses, plays a significant role. There are several AI systems that improve the control of
processes, the planning of production, and the early identification of faults [25]. Predictive
maintenance like condition-based monitoring, advanced data processing methods, and
better process control approaches are some of the technologies that should be considered
to belong under this category. The use of AI makes it possible to analyze past data and
identify trends, abnormalities, etc., which in turn provides direction for the development
of recommendations about the upkeep of material manufacturing processes. Several ML
techniques have been applied to improve material process parameters, find flaws and
defects, and project equipment failure. Using these fixes lowers downtime and helps to
reduce manufacturing costs [26]. AI-powered statistical quality control during material
processing has allowed companies to significantly reduce rework costs while preserving
high accuracy. Improving the efficiency of the industrial sector is one of the several objec-
tives of looking at the combined effects of Industry 4.0 and AI on material handling. By
means of the integration of several technologies included in Industry 4.0, industry partners
may better grasp the intricacy of the production process. This will help them optimize the
efficiency of machinery, tools, and other resources at hand, as well as manage raw materials
and monitor their processing across the workflow.
AM processes are developing swiftly to meet client expectations and provide improved
product quality as industry needs arise with new data processing technologies. Ashima et al.
have postulated that introducing the latest IoT developments into additive manufacturing
will help three-dimensional (3D) printing overcome its limitations. This article researched
improvements targeting automated 3D printing accuracy for improved surface finish and
tolerances, which is currently a problem for most AM processes. Automatic 3D printing
systems are a new research challenge, and there is little research on this topic [27]. By
introducing machine learning in AM apparatus aimed at implementing AI at an industrial
scale, the goal was to boost local manufacturing efficiency. The authors suggested that
further integration of AI-equipped AM equipment with Industry 4.0 cloud data would
improve production accuracy and rates. According to this research, AI implementation
at the local level provided many benefits to the factory, including the rapid identification
Machines 2024, 12, 681 4 of 27

of process parameters, the choice of application-suitable materials, initial product testing,

and improved safety throughout production [28]. AM’s speed, accuracy, reproducibility,
and cost may prevent it from being used in industrial facilities for the mass manufacture of
standard components. AM has extensive production possibilities because of its material
(polymers to metals), size (nanoscale to macroscale), and functionality (self-assembling
to excellent heat transfer) [29]. Even with the advanced method and material intended to
improve the internal structure and design, there is still a chance that these changes will have
a negative effect on the strength of the material. Combining AM and subtractive processes,
also called hybrid manufacturing, addresses some of these challenges and allows for part
repair and restructuring and product surface quality improvements. Decentralization
can also be adopted by using cloud services to distribute production across factories or
equipment, thereby allowing SMEs to participate in a global supply chain [30]. Digital
manufacturing and 3D printing will also impact society through innovative product design,
sustainability, and material efficiency [31]. Another study demonstrated that an AI model
monitored Wire + arc additive manufacturing (WAAM) welding processes can identify
defects and control feed rate, voltage, current, and process parameters in real time. The
ensemble model used here allows for the investigation of process parameter combinations
to provide improved fault identification. Generalized WAAM anomaly detection and
diversification models are needed for further study. Soon, strong AI models will combine
form and high-quality or reliable data [32].
Turning industrial operations into ever more intelligent, flexible, and efficient systems
naturally connects AI with Industry 4.0. Industry 4.0 combines digital technology—digital
twins, for example—with physical processes to create CPPS, which uses real-time data to
improve industrial processes. In this changing paradigm, AI is very crucial, as it makes
advanced data analysis possible, data anomaly detection practical, predictive maintenance
doable, process optimization possible, and other related uses conceivable. Computer
vision and machine learning technologies allow the development of well-informed choices
leading to decisions by way of thorough analysis of the enormous data produced by
Industry 4.0 systems. Industry 4.0’s synergy with AI drives real-time monitoring, anomaly
identification, and adaptive reactions, thereby boosting production. Furthermore, this
combination system may increase production, reduce operational interruptions, and attain
accuracy, thus enabling the development of intelligent factories.
The primary goal of this review article is to examine the potential methods by which
material processing might be improved by combining the capabilities of Industry 4.0 and AI.
Another goal is to provide a strategy for leveraging these advantages in current industrial
environments. This article assesses the practicality of incorporating some technologies
from Industry 4.0 into material processing methods. The main objective of this activity
is to ensure that participants have a comprehensive understanding of how Industry 4.0
technologies facilitate the real-time monitoring, control, and optimization of industrial
processes. Furthermore, this study will discuss how AI influences the efficiency of many
industrial facilities. This work aims to evaluate the use of AI in improving logistics
management, thereby providing quality assurance and optimizing processes. The article
also considers the different ways in which the integration of AI and technology linked
within Industry 4.0 can increase industrial productivity. In addition, there will be an
analysis of the problems brought about by the combination of AI and Industry 4.0 and
suggestions for how these problems may be overcome. Some emerging concepts, such as
cyber security and edge computing etc., will be covered in the latter section of this article.

2. Materials and Methods

The study’s methodology followed the recommendations provided by PRISMA, which
stands for Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Figure 2
depicts the research methodologies used in writing the paper. A literature review search
string was generated using the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) criteria. The papers were assessed using a framework developed
Machines
Machines 2024,
2024, 12,
12, xx FOR
FOR PEER
PEER REVIEW
REVIEW 55 of
of 28
28

Machines 2024, 12, 681 5 of 27

search
search string
string was
was generated
generated using
using the
the Preferred
Preferred Reporting
Reporting Items
Items for
for Systematic
Systematic Reviews
Reviews
and
and Meta-Analyses (PRISMA) criteria. The papers were assessed using aa framework
Meta-Analyses (PRISMA) criteria. The papers were assessed using framework de-de-
by identifying
veloped the key ideas key
associated associated
with resource efficiency, business divisions, and AI
veloped byby identifying
identifying the
the key ideas
ideas associated withwith resource
resource efficiency,
efficiency, business
business divi-
divi-
approaches
sions, and importantand aspects defining current research trends in these trends
areas. Figure 3
sions, and
and AI
AI approaches
approaches and important
important aspects
aspects defining
defining current
current research
research trends in
in these
these
illustrates
areas. the sequential process of the literature review study.
areas. Figure
Figure 33 illustrates
illustrates the
the sequential
sequential process
process of
of the
the literature
literature review
review study.
study.

Figure 2.
Figure 2. Research process.
Research process.

Identification
Identification of
of studies
studies via
via databases
databases

••
Identification

Records
Records removed
removed before
before screening
Identification

screening
Records •• Duplicate records removed
Records identified
identified from:
from: Duplicate records removed
Databases (n = 2090) •• Records
Records removed
removed for
for other
other reasons
reasons
Databases (n = 2090)
like
like language, inappropriate appli-
language, inappropriate appli-
cations
cations etc.
etc.

Records
Records screened
screened Records
Records excluded
excluded
(n = 725)
(n = 725) (n = 496)
(n = 496)
Screening
Screening

Reports
Reports sought
sought for
for retrieval Reports
retrieval Reports not
not retrieved
retrieved
(n = 229)
(n = 229) (n = 21)
(n = 21)

Reports
Reports assessed
assessed for
for eligibility
eligibility Reports
Reports excluded:
excluded:
(n = 208)
(n = 208) Does
Does not address
not address real
real world
world scenarios
scenarios
(n =33)
(n = 33)
Inappropriate
Inappropriate quality (n ==39)
quality (n 39)
Include

Studies
Studies included
included in
in review
Include

review
dd

(n = 136)
(n = 136)
Complete
Complete full
full article
article

Figure
Figure 3.
3. Flow
Flow diagram
diagram of
of research
research methodology
methodology and literature
and literature selection
literature selection process.
selection process.
process.
Machines 2024, 12, 681 6 of 27

2.1. Research Questions

A total of three research questions (RQ) were formulated in the present study as follows:
RQ1: What are the Industry 4.0 and AI technologies for improving material processing
efficiency?
RQ2: How can integrating Industry 4.0 with AI technologies optimize manufacturing
efficiency?
RQ3: What are the main challenges in adopting Industry 4.0 and AI technologies for
manufacturing, and what future trends and emerging technologies have been proposed to
overcome these challenges?

2.2. Search Strategy

Firstly, we performed a keyword search using the logical operators AND/OR in
Scopus. Subsequently, we conducted a search by employing relevant keywords in esteemed
publications such as Google Scholar, Emerald, MDPI, Sage, Research Gate, Wiley, Taylor &
Francis, and other credible sources. We subsequently assessed the titles and abstracts of all
the citations retrieved from our search to ascertain their potential significance. Subsequently,
we collected the pertinent citations to conduct a comprehensive analysis. A meticulous
examination of the bibliographies of all pertinent publications was conducted to conduct a
comprehensive literature search. Table 1 provides a comprehensive overview of the search
method, encompassing all pertinent facts and websites. In addition, we intended to reach
out to authors in cases where research appeared to meet the criteria but did not provide the
necessary data. This was performed to obtain supplemental information and, thus, increase
the number of studies included in our analysis.

Table 1. Study selection and databases.

Selection Criteria Database and Strategies

Search Boundary Google Scholar, Emerald, MDPI, Sage, Research Gate, Wiley, Scopus, Taylor & Francis
Core Keyword Industry 4.0, Artificial Intelligence (AI), Material Processing, Manufacturing Efficiency
1.”Industry 4.0” AND “Artificial Intelligence” AND “Material Processing”
2.”Internet of Things” AND “Material Processing” AND “Manufacturing Efficiency”
3. “Big Data Analytics” AND “Material Processing Optimization”
4. “Additive Manufacturing” OR “Robotics” AND “Industry 4.0”
Combined Search Phrases
6. “Machine Learning” AND “Process Optimization” OR “Material Processing”
7. “Deep Learning” AND “Defect Detection” AND “Material Processing”
8. “Reinforcement Learning” AND “Adaptive Manufacturing”
9. “AI-driven Predictive Maintenance” AND “Material Processing”

2.3. Study Selection (Inclusion and Exclusion Criteria)

We included all articles focusing on the utilization of AI and Industry 4.0 technologies
which were used in various manufacturing or production systems. Ethical approval was
not required because this study retrieved and synthesized data from already published
studies. Table 2. Illustrates the inclusion and exclusion process of the literature review for
the study.

Table 2. Study selection.

Inclusion Exclusion
Literature type Indexed journals, book chapters, conference proceeding Non-indexed journals, magazine articles
Timeline Between the years 2000 and 2024 Before the year 2000
Language English Non-English
Full text Accessible for detailed analysis Only abstract/summary
 Inclusion Exclusion
Non-indexed journals, magazine
Literature type Indexed journals, book chapters, conference proceeding
articles
Timeline Between the years 2000 and 2024 Before the year 2000
Language
Machines 2024, 12, 681 English Non-English 7 of 27
Full text Accessible for detailed analysis Only abstract/summary

3. Results
3. Results
3.1.
3.1. Year-Wise
Year-Wise Publication
Publication Progress
Progress
Figure
Figure 44illustrates
illustrates the
the yearly
yearly publication
publication progress
progress of of this
this topic,
topic, revealing
revealing aa trending
trending
nature
nature from 2015 to 2020, followed by a slight decline until 2023. While the reduction in
from 2015 to 2020, followed by a slight decline until 2023. While the reduction in
publications
publicationsisisnot
notsignificant,
significant,ititis
is noteworthy
noteworthythatthat aa significant
significant number
number of of researchers
researchersare
are
actively
actively engaged
engagedin inthis
thisfield.
field.

Yearly Publication Progress

20 19
18
16 15 15
Number of Publication

14 13 13
12 12
12 11
10
10
8
6
4
4
2
0
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
Year of Publication

Figure4.4. Publication
Figure Publication progress.
progress.

3.2.
3.2. Highly
Highly Cited
Cited Papers
Papers (Global
(Global Citations)
Citations)
Table
Table 3 presents a summary and
3 presents a summary and the
the primary
primary contribution
contribution of
of each
each highly
highly cited
cited paper
paper
under
under consideration for review. From this table, it is clear that many authors fromall
consideration for review. From this table, it is clear that many authors from allover
over
the
the world
world are
areworking
working to to tackle
tackle new
newchallenges
challenges andand create
createnew
newopportunities
opportunities in
in this
this area
area
to
to improve
improvemanufacturing
manufacturingefficiency.
efficiency.

Table3.3.Most
Table Mostcited
citedpapers.
papers.

Author Author Contribution

Contribution Total Citations
Total Citations
Addressing
Addressing the the current
current challenges challenges
of product ofdata
life cycle product life cycle
management data
and
Tao et al., 2018 2825
[51] introducing amanagement and introducing
digital twin-driven a digitalmanufacturing
approach to improve twin-driven efficiency.
approach to 2825
improve
Providing the evolution and future manufacturing
direction efficiency.
of manufacturing systems by discussing
Monostori et al., 2016 the integrationProviding
of CPS andthe evolution
CPPS, andhistorical
identifying future direction of manufacturing
contributions and current 1981
[64] challenges.
systems by discussing the integration of CPS and CPPS, identi- 1981
Providing an in-depth fying
surveyhistorical
of Supportcontributions
Vector Machineand current
(SVM) challenges.
applications in machine
Widodo et al., 2007 1842
condition monitoring and fault diagnosis, comparing it with other intelligent systems
Emphasizing the role of deep learning in handling big manufacturing data,
Wang et al., 2018 characterized by high volume, velocity, and variety, enabled by the widespread 1620
deployment of sensors and IoT
Providing an in-depth analysis of the role of big data and digital twin (DT) in smart
Qi et al., 2018 1603
manufacturing
Summarizing a historical review of the contributions made by the International
Teti et al., 2010 Academy for Production Engineering in the field of sensor monitoring of future 1545
machining operations
 ety, enabled by the widespread deployment of sensors and IoT
Providing an in-depth analysis of the role of big data and digi-
[61] 1603
tal twin (DT) in smart manufacturing
Summarizing a historical review of the contributions made by
Machines 2024, 12, 681
[56] the International Academy for Production Engineering in the 1545 8 of 27
field of sensor monitoring of future machining operations

3.3. Most
3.3. Most Productive
Productive Journals
Journals
Figure 5
Figure 5 shows
shows the
the journal's
journalʹs name
name with
with their
their publishing
publishing article
article number
number which
which has
has
been published
been published covering
covering this
this topic.
topic.

Publication Details

IEEE Transactions on Industrial Electronics

Journal Name

IEEE Access
Energies
Sensors
Applied Science
CIRP, The International Academy for Production Engineering
Journal Manufacturing Systems
The International Journal of Advanced Manufacturing Technology

0 1 2 3 4 5 6
Publication Number

Figure 5. Journal-wise
Figure 5. Journal-wise publication
publication number.
number.

4. Discussion
What are the Industry 4.0 and AI technologies for improving material processing
RQ1: What
efficiency?
efficiency?

4.1. Industry
4.1. Industry 4.0
4.0 Technologies
Technologies in
in Material
Material Processing
Processing
Industry 4.0 has emerged as a newindustrial
Industry 4.0 has emerged as a new industrialparadigm,
paradigm, where
wherethethe
implementation
implementation of
various technologies
of various technologiesprovides
providesa digital capability
a digital to network,
capability monitor,
to network, andand
monitor, control factory
control fac-
environments. It offers a complex technological structure that transforms traditional
tory environments. It offers a complex technological structure that transforms traditional
material processing
material processing techniques
techniques into
into smart
smart manufacturing systems. Figure
manufacturing systems. Figure 66 provides
provides anan
overview of various Industry 4.0 technologies [33,34]. It combines technologies to provide
overview of various Industry 4.0 technologies [33,34]. It combines technologies to provide
a digitally managed solution for real-world material processing. It can gather real-time
a digitally managed solution for real-world material processing. It can gather real-time
data, monitor the process from a distance, and adjust the process parameters involved in
data, monitor the process from a distance, and adjust the process parameters involved in
the process correctly. Application of such technologies in various manufacturing processes
the process correctly. Application of such technologies in various manufacturing pro-
helps producers improve product quality, reduce waste, respond quickly, save energy,
cesses helps producers improve product quality, reduce waste, respond quickly, save
and so on. Table 4 illustrates the various Industry 4.0 technologies and their impact on
enhancing manufacturing efficiency in the material processing fields. Reducing material
waste, allowing for mass customization, and allowing more complex geometries—which
so reduces lead times and production costs—AM helps to improve efficiency [35,36]. This
is quite beneficial in sectors like aerospace and automotive, where precision and material
economy are vital. Robotics, when tied into AM, offer manufacturing flexibility by reducing
uncertainty, reducing human error, and facilitating autonomous task execution. In high-
volume companies, this is vital as it results in faster reaction times and high degrees of
accuracy and repeatability. Big data analytics, cloud computing, IoT, etc., serve to allow
condition monitoring, energy-efficient optimization, and better decision-making all through
the production lifecycle by means of a deeper understanding of manufacturing processes.
Improved process control, real-time monitoring, and predictive analytics, all of which
 and material economy are vital. Robotics, when tied into AM, offer manufacturing flexi-
bility by reducing uncertainty, reducing human error, and facilitating autonomous task
execution. In high-volume companies, this is vital as it results in faster reaction times and
high degrees of accuracy and repeatability. Big data analytics, cloud computing, IoT, etc.,
Machines 2024, 12, 681 9 of 27
serve to allow condition monitoring, energy-efficient optimization, and better decision-
making all through the production lifecycle by means of a deeper understanding of man-
ufacturing processes. Improved process control, real-time monitoring, and predictive an-
are strategies
alytics, all of which spanning
are strategies major Industry
spanning 4.0 technologies,
major Industry ultimately
4.0 technologies, raise manufacturing
ultimately
efficiency.
raise manufacturing efficiency.

Figure 6. Overview of Industry

Figure 4.0 technologies.
6. Overview of Industry 4.0 technologies.

Table 4. Industry 4.0 technologies and their related contributions.

Table 4. Industry 4.0 technologies and their related contributions.
ustry 4.0 Technologies Description Contribution References
Industry 4.0 Technologies Description Contribution References
AM is an advanced manufacturing
AM
process that is is an on
based advanced manufacturing
the addition of Wasteprocess
reduction, cost saving, infi-
that is based on the addition of material to Waste reduction, cost saving, infinite
AM AM material to pre-determined path plans nite part complexity, and material [35–37] [35–37]
pre-determined path plans to produce part complexity, and material flexibility
to produce three-dimensional (3D) ge-
three-dimensional (3D) geometries. flexibility
ometries.
AI-driven robot to handle production tasks Quick response time, precision,
Advanced Robotics [38–41]
and task uncertainty autonomously. repeatability, error reduction
Augmented Virtual (AV) AV and VR bridge virtual experiences with
Quality improvement, productivity [42–44]
and Virtual Reality (VR) the real world.
Cloud computing represents on-demand Data management, on demand,
Cloud Computing services that provide storage and even eliminates error and ease of repetitive [45–48]
computational services over the internet. operations
IoT is a collective and collaborative
Precise process control, remote
IoT network established between various [49,50]
monitoring higher quality
machines, tools, clouds and devices.
Productivity improvement, condition
Big data is used to gather insights into
monitoring, and energy-efficient
Big Data processes and assimilation that lead to [51,52]
optimization over manufacturing
decisions, investigations, etc.
execution lifecycles
 Machines 2024, 12, 681 10 of 27

Table 4. Cont.

Industry 4.0 Technologies Description Contribution References

Data analytics is a process of extraction of
Data Analytics meaningful data to propose corrective Process improvement, fault detection [53–55]
actions and detect long-term trends.
AMM refers to real-time condition
Advanced Monitoring and monitoring of equipment, which detects Part quality, repeatability, preventative
[56,57]
Maintenance (AMM) early failure conditions and predicts maintenance
failures pre-emptively
Simulation is a physics-based advanced
computational tool used for virtual
Simulations/Analysis of Cost and time saving, process
representation of real-world applications to [58,59]
Virtual Models improvement
predict process and material behavior that
saves money, effort, and time.

4.1.1. IoT in Material Processing

IoT enables electronics included in or subsequently added to physical machines or
systems to gather real-time data and network them within cyberspace. It facilitates the
connection of multiple manufacturing resources and promotes horizontal integration. This
opens a new opportunity for conventional or new manufacturing services to improve
manufacturing efficiency [60]. The framework of an IoT-based smart manufacturing system
Machines 2024, 12, x FOR PEER REVIEW
is shown in Figure 7. It provides a system where IoT can network with different facilities
and customers to meet the customer specifications.

Figure
Figure 7.7.IoT
IoT based
based smartsmart
factoryfactory
based on based
the dataon the[60].
in Ref. data in Ref. [60].
4.1.2. Leveraging Big Data Analytics for Material Processing Optimization
4.1.2.Big
Leveraging
data analyticsBig
for Data Analytics
material processingfor Materialconsists
optimization Processing Optimization
of multiple phases,
including data collecting via IoT devices and sensors. To enable accessibility and analysis,
Big data analytics for material processing optimization consists of mu
several data streams, for example, temperature, pressure, and material qualities, are gath-
including data collecting via IoT devices and sensors. To enable accessibility
several data streams, for example, temperature, pressure, and material quali
ered and combined with a central system. The fusion of DT and big data, as
Figure 8, was utilized to expedite the product life cycle during the design
 4.1.2. Leveraging Big Data Analytics for Material Processing Optimization
Big data analytics for material processing optimization consists of multiple
including data collecting via IoT devices and sensors. To enable accessibility and a
Machines 2024, 12, 681
several data streams, for example, temperature, pressure, and material11qualities, of 27
ar
ered and combined with a central system. The fusion of DT and big data, as illustr
Figure
ered and8,combined
was utilized to expedite
with a central system.the
Theproduct lifeand
fusion of DT cycle during
big data, the design
as illustrated in stage
central
Figure 8,to the
was concept
utilized of manufacturing
to expedite the product lifeas a service
cycle anddesign
during the service-oriented
stage, a goal cen- smart m
tral to the concept of manufacturing as a service and service-oriented smart
turing. This data fusion resulted in the promotion of smart manufacturing, whi manufacturing.
This data fusion resulted in the promotion of smart manufacturing, which was beneficial to
beneficial to all aspects of production, like quick product design, production pl
all aspects of production, like quick product design, production planning, and predictive
and
maintenance [61].maintenance [61].
predictive

Figure
Figure 8.8.Fusion
Fusion among
among big data,
big data, digitaldigital twin,
twin, and andinservices
services in manufacturing
manufacturing based
based on the data in on the
Ref. [61].
Ref. [61].
Big data can be used to sample information from various engineering areas like design,
manufacturing, quality, testing, etc. Design plays an important role in manufacturing
processes. Using big data, the design stage is currently transitioning from a subjective
conceptual scope to a data-driven one. Big data provides the ability to refine the design,
prompt customer response, and promote innovation. Quality plays a crucial role in reducing
rework, which in turn boosts productivity. Raw material quality and process parameters
are the most important parameters in measuring product quality. Figure 9 [62] discusses
the use of big data to measure product quality and improve design stages during material
processing. Through its implementation, it can easily be understood that the time involved
in the design stage has been decreased, and it allowed more adaptability in the design
stages through the assimilation of inputs from customer demand and historical design.
 eters are the most important parameters in measuring product quality. Figure 9 [62] dis-
cusses the use of big data to measure product quality and improve design stages during
material processing. Through its implementation, it can easily be understood that the time
involved in the design stage has been decreased, and it allowed more adaptability in the
Machines 2024, 12, 681 12 of 27
design stages through the assimilation of inputs from customer demand and historical
design.

Figure 9. Big data application in quality measurement based on the data in Ref. [62].
Figure 9. Big data application in quality measurement based on the data in Ref. [62].

4.1.3. CPS’s Role in Material Processing

CPS is the paradigm of turning a group of physical material processing machines
into a flexible production environment by combining data from sensors and actuators
installed into and in the immediate surroundings of machines. Analyzing this data helps
to optimize process parameters, therefore enhancing the process efficiency and quality.
Modern algorithms and embedded sensing enable the widespread implementation of these
technologies. The integration of predictive analytics and ML with CPS further improves
the ability to forecast productivity and functionality. Furthermore, researchers in the field
have expressed significant concerns about the preferred integration method of DTs and
 into and in the immediate surroundings of machines. Analyzing this data helps to opti-
mize process parameters, therefore enhancing the process efficiency and quality. Modern
algorithms and embedded sensing enable the widespread implementation of these tech-
nologies. The integration of predictive analytics and ML with CPS further improves the
Machines 2024, 12, 681 ability to forecast productivity and functionality. Furthermore, researchers in the
13 of 27 field
have expressed significant concerns about the preferred integration method of DTs and
CPS. There are two major aspects of manufacturing using CPS and DTs: physical machines
CPS. There are two major aspects of manufacturing using CPS and DTs: physical machines
and the virtual or digital environment [63]. CPS and DTs can improve manufacturing sys-
and the virtual or digital environment [63]. CPS and DTs can improve manufacturing sys-
tems’ resilience, intelligence, and efficiency by creating feedback loops that allow physical
tems’ resilience, intelligence, and efficiency by creating feedback loops that allow physical
processes
processestotoinfluence
influencecyber parts and
cyber parts andvice
viceversa
versa [64].
[64]. In In addition
addition to ato a variety
variety of widely
of widely
accessible services
accessible servicesandandapplications, thecyber
applications, the cyberoror digital
digital realm
realm encompasses
encompasses the intelligent
the intelligent
management
managementofofinformation, analysis,and
information, analysis, andcomputational
computational capabilities.
capabilities. Figure
Figure 10 illustrates
10 illustrates
thethe connectionbetween
connection between physical
physical entities
entitiesand
andthe cyber
the cyberworld through
world sensors
through and data
sensors and data
management.
management.

Figure 10.10.
Figure Cyber–Physical
Cyber–PhysicalSystem basedon
System based onthe
thedata
datain in Ref.
Ref. [63].
[63].

4.1.4.
4.1.4. AM AM andRobotics
and RoboticsIntegration
Integration in
inIndustry
Industry4.04.0
The integration of AM and robotics in manufacturing processes has enabled the
The integration of AM and robotics in manufacturing processes has enabled the pro-
process to be more effective. Several types of AM are available to optimize production
cess to be more
efficiency effective.
[35], which Several
has been types
shown of AM11.
in Figure are available to optimize production effi-
ciency [35], which has been shown in Figure 11.
AM allows the creation of complex geometries that are impossible and, at other times,
significantly time-consuming in traditional manufacturing [65]. It enables the production
of the final product through the layer-by-layer deposition of material using either wire
or powder [66]. With robots handling repetitive tasks, a modern factory combining AM,
SM and robots runs smoothly and reduces labor cost and time, increasing productivity.
More importantly, additive manufacturing (AM) can optimize the material usage rate,
leading to waste reduction. The use of robots in these steps aids in precision and safety and
reduces errors [67,68], as illustrated in Figure 12. Using concurrent structure and process
optimization leads to a 21% reduction in manufacturing costs and a 21% percent faster
build time by adopting the cost minimization framework, as shown in Figure 12 [69].
 Liquid Multi-jet Powder Fused
Thermal Moulding Spray Bed Deposition Laminated
Machines 2024, 12, x FOR PEER REVIEW Bed Modeling Object
14 of 28
Polymer
Machines 2024, 12, 681 14 of 27
Manufacturing
ization

State of Raw
Selective
Materials
Electron Selective
3D Direct Laser Beam Laser
Printing Energy Melting Melting Sintering
Deposition
Solid
Figure 11. Classification of AM based on Powder
Liquid the data in Ref. [35]. Filament
Layer

AM allows the creation of complex geometries that are impossible and, at other times,
Liquid
Sterolith Multi-jet Powder Fused
significantly
Thermaltime-consuming
Moulding in Spray
traditional manufacturing [65].
Bed It enables the production
Deposition Laminated
ography Bed Modeling Object
Polymer
of the final product through the layer-by-layer deposition of material using either wire or
Manufacturing
ization
powder [66]. With robots handling repetitive tasks, a modern factory combining AM, SM
and robots runs smoothly and reduces labor cost and time, increasing productivity. More
importantly, additive
3D manufacturing Direct (AM) can Laser optimize the Electron
Selective Selective
material usage rate, leading
Beam Laser
Printing The use of robots
to waste reduction. Energy in these Melting
steps aids in precision
Melting andSintering
safety and re-
Deposition
duces errors [67,68], as illustrated in Figure 12. Using concurrent structure and process
optimization leads to a 21%
Figure
reduction in manufacturing costs and a 21% percent faster
Figure11.
11.Classification
ClassificationofofAM
AMbased
basedonon
the data
the inin
data Ref. [35].
Ref. [35].
build time by adopting the cost minimization framework, as shown in Figure 12 [69].
AM allows the creation of complex geometries that are impossible and, at other times,
significantly time-consuming in traditional manufacturing [65]. It enables the production
of the final product through the layer-by-layer deposition of material using either wire or
powder [66]. With robots handling repetitive tasks, a modern factory combining AM, SM
and robots runs smoothly and reduces labor cost and time, increasing productivity. More
importantly, additive manufacturing (AM) can optimize the material usage rate, leading
to waste reduction. The use of robots in these steps aids in precision and safety and re-
duces errors [67,68], as illustrated in Figure 12. Using concurrent structure and process
optimization leads to a 21% reduction in manufacturing costs and a 21% percent faster
build time by adopting the cost minimization framework, as shown in Figure 12 [69].

Figure 12. Cost Optimization framework

Figure 12. Cost based
Optimization on thebased
framework dataon
inthe
Ref. [69].
data in Ref. [69].

4.2. Enhancing Manufacturing Efficiency with AI

4.2. Enhancing Manufacturing Efficiency with AI
Many disciplines have been touched by the AI revolution and its many enabling
Many disciplines have been
technologies. touched
Among otherby the AI revolution
advantages, and its
AI has greatly many
helped to enabling tech-
lower waste, boost
production efficiency, and lower downtime in the manufacturing
nologies. Among other advantages, AI has greatly helped to lower waste, boost sector. Figure 13 shows
the main
Figure AI technologies
12. Cost that are helping
Optimization framework based ontothe
shape future
data in smart manufacturing systems.
Ref. [69].
Table 5 lists the main AI technologies together with how they affect the pertinent sector.
4.2. Enhancing Manufacturing Efficiency with AI
Many disciplines have been touched by the AI revolution and its many enabling tech-
nologies. Among other advantages, AI has greatly helped to lower waste, boost
Machines 2024, 12, x FOR PEER REVIEW 15 of 28

production efficiency, and lower downtime in the manufacturing sector. Figure 13 shows
Machines 2024, 12, 681 15 of 27
the main AI technologies that are helping to shape future smart manufacturing systems.
Table 5 lists the main AI technologies together with how they affect the pertinent sector.

Figure 13.
Figure 13. Key
Key components
components of
of AI.
AI.

Table5.5. Mostly
Table Mostly used
used AI
AI tools
tools and
and their
their contributions.
contributions.

AI Model Name Description Contribution References

AI Model Name Description Contribution References
Regression Process optimiza-
Regression Using
Using labeled
labeled data
data for usefor
in use
manyin Process optimization
Supervised
SupervisedLearn- tion for parameters,
Learning
many applications
applications like like predic-
prediction, for parameters, quality [70,71]
[70,71]
ing Classification
Classification quality improve-
classification
tion, classification improvement
ment
Using unlabeled data which can Effective way of data
Unsupervised Using unlabeled
discover insights anddatadatawhich can mining, Identification
patterns Effective way of [72,73]
Learning
Unsupervised discover
without any insights
explicitand data pat-
guidance of defects
data mining, Identi- [72,73]
Learning Self-Training terns without any explicit guid-
Semi- fication
In of defects
situ quality control,
Supervised Low-Density Separation Model Handling mixedance data, which have
automatic fault [74,75]
Learning Self-Training some labeled and
Handling mixedsome unlabeled
data, which In situdetection
quality con-
Semi-Supervised Graph-Based Algorithm
Low-Density Separation Model have some labeled and some trol, automatic fault [74,75]
Learning Dynamic Programming Prior process
Graph-Based Algorithm Using AI agents, unlabeled
collects own data detection
Reinforcement optimization, solving
Monte Carlo Methods
Dynamic Programming and improves data through Prior process opti- [76,77]
Learning complex material
Using AI agents, collects own
trial-and-error
Reinforcement Heuristic Methods
Monte Carlo Methods mization, solving
handling situations
data and improves data through [76,77]
Learning Deep Feed Forward Networks complex material
Heuristic Methods trial-and-error
Convolution Neural Networks handling
Material situations
synthesis,
Deep Feed Forward
(CNN) Networks material removal rate
Subset of ML algorithm that can Material synthesis,
prediction, process
Deep Learning Convolution
Recurrent Neural Networks
Neural Networks (RNN) solve real-world problems without [78–82]
failure prediction,
material removal
(CNN) Subset supervision
of ML algorithm that
Transformers material
rate performance
prediction, pro-
Deep Learning Recurrent Neural Networks can solve real-world problems prediction [78–82]
Graph Neural Network cess failure predic-
(RNN) without supervision
tion, material per-
Transformers
These Network formance
technologies influence smart systems significantly. Two machine prediction
learning mod-
Graph Neural
els that can maximize productivity and reduce industrial waste by parameter adjustment
are dynamic programming and regression. Identification of faults in real-time and preven-
tion of significant production problems before they cause catastrophic failure to depend on
CNNs and unsupervised learning models. By means of their impact, deep learning models
 These technologies influence smart systems significantly. Two machine learning
models that can maximize productivity and reduce industrial waste by parameter adjust-
Machines 2024, 12, 681 ment are dynamic programming and regression. Identification of faults in real-time and
16 of 27
prevention of significant production problems before they cause catastrophic failure to
depend on CNNs and unsupervised learning models. By means of their impact, deep
learning
assist models assist
in foreseeing in foreseeing
material behavior material behavior
at different at different
production phases,production phases,
thereby reducing
thereby reducing failures and increasing product quality. Robotic and material
failures and increasing product quality. Robotic and material handling automation of handling
automationactivities
challenging of challenging activities
also helps alsoproduction
to boost helps to boost production
by using by usinglearning
reinforcement reinforcement
meth-
ods [75–77]. For the benefit of future manufacturing environments, AI technologyAI
learning methods [75–77]. For the benefit of future manufacturing environments, tech-
is vital
nology
for is vital
achieving for achieving
predictive predictive
maintenance, maintenance,
real-time real-time
monitoring, and monitoring, and complete
complete automation—all
automation—all
essential componentsessential components
of smart industrialofsystems.
smart industrial systems.

4.2.1. ML
4.2.1. ML Algorithms
Algorithms for
for Process
ProcessOptimization
Optimization
Theapplication
The applicationofofML MLalgorithms
algorithmstotomanufacturing
manufacturing processes
processes hashas been
been rapidly
rapidly gain-
gaining
popularity in the
ing popularity inrecent past.past.
the recent The The
development
development of MLof algorithms
ML algorithms suchsuch
as linear regres-
as linear re-
sion [83],[83],
gression random
random forest (RF)(RF)
forest [84], decision
[84], decisiontrees [85],
trees [85],SVM
SVM[86],
[86],neural
neuralnetworks
networks [87],
[87],
clustering
clusteringalgorithms
algorithms[88],[88],etc. provides
etc. provides thethe
opportunity
opportunity to establish a relationship
to establish between
a relationship be-
input process parameters and output results [89]. Each of these algorithms
tween input process parameters and output results [89]. Each of these algorithms has its has its own
characteristics best suited
own characteristics to dealing
best suited with with
to dealing different kindskinds
different of manufacturing
of manufacturingproblems. The
problems.
major applications
The major of MLofalgorithms
applications ML algorithms are fault
are analysis [90], process
fault analysis monitoring,
[90], process condition
monitoring, con-
monitoring, and tooland
dition monitoring, wear
tool[91]. SVM
wear is used
[91]. SVMtoisidentify
used tofaulty
identifyproducts
faulty on a manufacturing
products on a man-
line. Figure 14 illustrates how several models, such as artificial neural networks
ufacturing line. Figure 14 illustrates how several models, such as artificial neural networks (ANN),
support vector machines (SVM), and radio frequency (RF), are utilized
(ANN), support vector machines (SVM), and radio frequency (RF), are utilized in numer- in numerous pro-
duction sectors to monitor the quality of components from their
ous production sectors to monitor the quality of components from their product range product range [89–96].
This information
[89–96]. is used to is
This information predict
used to tool wear based
predict on historical
tool wear based ondata, ultimately
historical data,preventing
ultimately
catastrophic failures and failures
preventing catastrophic improving andefficiency.
improving efficiency.

Figure 14. Comparison of observed and predicted values of tool wear using (a) ANN, (b) SVM,
(c) RF, (d) Tool wear prediction using SVM based on the data in Ref. [97].

4.2.2. Deep Learning Applications in Material Processing

Modern AI methods like deep learning have the potential to be applied for predic-
tive modeling and quality control. Based on their material composition, metallurgical
 Machines 2024, 12, x FOR PEER REVIEW 17 of 28

Figure 14. comparison of observed and predicted values of tool wear using (a) ANN, (b) SVM, (c)
RF (d) Tool wear prediction using SVM based on the data in Ref. [97].
Machines 2024, 12, 681 17 of 27
4.2.2. Deep Learning Applications in Material Processing
Modern AI methods like deep learning have the potential to be applied for predictive
modeling
informationand can
quality control. Based
be leveraged on their material
to estimate composition,
the mechanical metallurgical
characteristics ofinfor-
certain alloys.
mation
Studies [98] have shown the great degree of accuracy and precision that Stud-
can be leveraged to estimate the mechanical characteristics of certain alloys. deep learning
ies [98] have
displays in shown the great
classifying degree spotting
products, of accuracy and precision
defects, that deep learning
and identifying displays
defect mechanisms. Deep
in
learning models like CNN and RNN may help in diagnosing machinerylearn-
classifying products, spotting defects, and identifying defect mechanisms. Deep problems and
ing models like CNN and RNN may help in diagnosing machinery problems and prog-
prognostic defects [99–103]. Furthermore, a deep learning model can teach itself and fore-
nostic defects [99–103]. Furthermore, a deep learning model can teach itself and forecast
cast target output with great accuracy and dependability by using sensor information. As a
target output with great accuracy and dependability by using sensor information. As a
result, a deep learning model optimizes process parameters as it builds the relationship
result, a deep learning model optimizes process parameters as it builds the relationship
between
between process
process parameters
parameters and output
and output results.results. Thesecan
These models models
predictcan predict
noble noble mate-
materials
rials
like like metals
metals with
with better better and
accuracy accuracy
make anand make
impact onan impact
new ondesign
material new material design [104].
[104]. Figure
Figure
15. shows15.theshows
generalthe general
procedure of procedure of material
material discovery using discovery using where
an ML algorithm, an ML thealgorithm,
whereoptimizes
model the model theoptimizes the process
process parameters parameters
considering considering
material componentsmaterial components and
and structural
properties.
structural properties.

Figure15.
Figure 15. Overview
Overview of material
of material discovery
discovery by MLby ML on
based based on the
the data data[104].
in Ref. in Ref. [104].

4.2.3.Reinforcement
4.2.3. Reinforcement Learning
Learning (RL)(RL) for Adaptive
for Adaptive Manufacturing
Manufacturing SystemsSystems
RLRLisisa subset
a subset of ML
of ML models
models usedused for levels
for high high levels of flexibility
of flexibility and responsiveness
and responsiveness to to
changingconditions
changing conditions in adaptable
in adaptable manufacturing
manufacturing systems systems [105]. Today’s
[105]. Today’s world revolves
world revolves
aroundincreased
around increased demand
demand uncertainty,
uncertainty, customization,
customization, agility,agility,
and theand theRLuse
use of of RL to adapt
to adapt
tochanging
to changing conditions
conditions andand minimize
minimize wastewaste in production
in production systems.systems.
One of theOnekeyofad-
the key ad-
vantages
vantagesofof RLRLin in
manufacturing
manufacturingsystems is thatisitthat
systems can it
handle scheduling
can handle problemsproblems
scheduling and and
improve manufacturing systems [106–108]. Previously, various heuristic
improve manufacturing systems [106–108]. Previously, various heuristic models and math- models and
mathematical
ematical models models wereemployed
were employed to to enhance
enhance theirtheirefficiency.
efficiency.TheThe
current advance-
current advancement
ment
of RL dramatically changes the scheduling environment by consideringcontin-
of RL dramatically changes the scheduling environment by considering the the continuous
uous interaction between variables such as machine setup time, job priorities, processing
interaction between variables such as machine setup time, job priorities, processing time,
time, and so on [109–111]. Eventually, the RL model “reinforced” itself to track sensor data
and so on [109–111]. Eventually, the RL model “reinforced” itself to track sensor data and
and modify process parameters, hence minimizing faulty products. Furthermore, RL
modify
helps process
robots performparameters, henceinminimizing
assembly duties faulty products.
a product assembly line so theyFurthermore,
may adapt the RL helps
robots perform assembly duties in a product assembly line so they may adapt the actuation
of their robotic arms in real-time using a feedback control system as part of a sophisticated
manufacturing system [112,113].
RQ2: How integrating Industry 4.0 with AI technologies can optimize manufacturing
efficiency in material processing?

5. Integration and Synergies between Industry 4.0 and AI

The incorporation of Industry 4.0 with AI has the potential to significantly enhance
industrial efficiency in several areas of material handling. Industry 4.0 uses a variety of
Machines 2024, 12, 681 18 of 27

technologies to collect data from multiple manufacturing lines within a manufacturing

organization. By using AI, these data might be used to spot aberrant trends and faulty
components and detect machine breakdowns pre-emptively. Table 6 shows how Industry
4.0 and AI technologies could be used in industrial operations together. For a computer
numerical control system, for example, the spindle’s state is vital. The use of AI with
Industry 4.0 offers a complete solution for real-time performance prediction and moni-
toring of a machine tool’s spindle condition. This has moderately increased production
efficiency and helped to reduce notable failures [114]. Integration of Industry 4.0 with
AI will also help spot anomalies in predictive maintenance in a timely manner. Different
modern AI techniques enable early and accurate anomaly identification, therefore lowering
the frequency of catastrophic failures that can compromise the reliability of the system
and improve output [115]. Big data-based predictive maintenance has been proposed for
structuring multi-source data and heterogeneous information to handle machine failure
while keeping the system's operability stable. This integration’s effectiveness has been
verified, and it can predict the remaining life of several parts of that system [116]. Fur-
thermore, in a production company, data-driven management can improve productivity.
The data management model applied in three different case studies and measured data
productivity ranged between 30% and 45%, and the overall equipment efficiency is near
85%, which indicates that a suitable method can improve the measurement process of data
in the industry 4.0 context [117].

Table 6. Impact of integration of Industry 4.0 and AI technologies.

AI Technologies Industry 4.0 Technologies Main Contribution References

Logistics Regressor
CNN IoT Anomaly data detection and predict future anomalous data [118,119]
RNN
IoT
CPS Design improvements by a data-driven approach, predictive
ML [120–123]
Cloud Computing maintenance for early fault detection, product assessment
Big Data Analysis
IoT Real-time predictive analytics to visualize warnings, errors,
ML [122]
CPS and faults of manufacturing systems
IoT
Condition-based real-time monitoring and predictive
ML CPS [124]
maintenance in a manufacturing line
Cloud Computing
Big Data Anomaly detection and establishment of a method of feature
ML [125]
IoT extraction
Long Short-Term Memory
Gated Recurrent Unit IoT Spur gear faulty accuracy prediction in a manufacturing line [126]
SVM
SVM
IoT
Decision Trees
Cloud Computing Product line failure root cause analysis, assembly line fault
Random Forests [125,127]
Simulation detection, and real-time early detection
ANN
CPS
MLP
IoT
Employed deep learning method for early fault detection in the
CNN Cloud Computing [127,128]
manufacturing line
Simulation
Cloud Computing
CNN Quality control [129,130]
IoT
SVM
Decision Trees Big Data Material classification and discovery in the raw material
[131,132]
RF IoT industry
KNN

The adoption of technologies, including AM, big data analytics, and IoT, has the
capacity not only to improve efficiency but also to support more sustainable industrial
Machines 2024, 12, 681 19 of 27

practices as Industry 4.0 technologies and AI-driven processes continue to transform

manufacturing [133]. Emerging AM is intrinsically more economical than conventional
manufacturing since it can maximize the deposition of raw materials to create a final
shape that directly contributes to environmental sustainability by conserving raw materials
and minimizing industrial waste [134]. It also reduces the amount of postprocess work
needed, which reduces the waste of raw materials, addressing resource utilization and
economic concerns [134]. Using AI for predictive maintenance and process optimization
helps manufacturers greatly lower energy usage by adjusting their process parameters. This
helps manufacturing sites lower their carbon impact in line with world sustainability targets.
Big data analytics, circular economy, and AI combined in supply chain management lower
greenhouse gas emissions associated with manufacturing and transportation. Furthermore,
the synergy effect of several technologies can decrease resource depletion and lower the
environmental impact of product life cycles, therefore supporting a more sustainable
manufacturing ecosystem [135,136].
RQ3: What are the main challenges adopting Industry 4.0 and AI technologies for manufac-
turing in material processing and what future trends and emerging technologies have been
proposed to overcome these challenges?

6. Challenges, Opportunities, and Future Directions

6.1. Addressing Challenges of Adoption of Industry 4.0 and AI
• Integration and compatibility of technology
Most of the industrial manufacturing processes will involve many linked systems,
including enterprise resource planning, programming logic controllers, supervisory control
from remote areas and data acquisition, DTs, IoT devices, and AI-driven platforms. Thus,
the integration of technologies can be challenging. Given the different communication
protocols, data formats, and operating standards across such platforms, seamless com-
munication among them would be a major obstacle. In real-time data management and
decision-making, consistency, quality, and accuracy will be challenging as every system
produces unique data.
• Cybersecurity risks and data privacy
Industry 4.0 connects large numbers of devices, coupled with AI-driven analytics,
cloud computing, and other technologies that greatly expand the attack points for cyber-
attacks. Moreover, many industrial organizations depend on legacy systems and equipment
not intended with contemporary cyber security in mind, so replacing existing systems with
new ones presents both logistical and economic problems.
• Skills deficit and workforce development
Constant technological developments call for staff to be always learning and evolving.
Managing complicated algorithms, data analytics, and operational process integration will
be needed for either knowledge in one sector or competence in another. The most difficult
features of implementation of these systems might be fear of job displacement, opposition
to change, and inexperience with tools.
• Feasibility study of finance
Implementation of the system will necessitate an assessment of upfront technological
expenses, including initial investment in hardware, software, infrastructure updates, and
training. For a manufacturing firm, cost-benefit analysis, net present value study, and
internal rate of return can determine if such a system is practical or not.
• Scalability and adaptability
There is a significant need to evaluate the increasing demand and ambiguity of the
procedures involved in this system. In an atmosphere of uncertainty, how will this future
Machines 2024, 12, 681 20 of 27

system manage several procedures, machines, and stakeholders? The key will be maintain-
ing operational agility, promoting sustainable development, and grabbing possibilities in
an increasingly competitive world.
• Real-time data processing and analysis
In an Industry 4.0 environment, the volume of data generated will be exceptionally
large, so handling it can be challenging, potentially creating bottlenecks in various manu-
facturing processes. Furthermore, data integration and data transformation capabilities
will be difficult because the data will come from a variety of sources, such as machines,
products, IoT sensors, and so on.
• Supply chain integration and collaboration
Emerging distributed digital factories enable various factories from all over the world
to be brought under one umbrella. Involving multiple suppliers, manufacturers, distribu-
tors, and other stakeholders across different geographic regions will make any supply chain
more complicated. A lack of integrated data systems and standardized communication
channels can lead to inefficiencies, inaccuracies, and delays in information insight into the
systems.

6.2. Future Directions: Emerging Trends

• Edge computing
The reduction of latency and the improvement of security will make it feasible for in-
dustrial manufacturing processes to perform real-time data processing or decision-making.
Currently, technological advancements are continuing to occur at a breakneck speed, which
makes this real-time decision-making feasible. As a result of the provision of predictive
maintenance, it improves the effectiveness of the production line, optimizes the efficiency of
the resources that are available for raw materials, and considerably increases the availability
of the machine.
• DTs
DTs create virtual replicas of physical assets, processes, and systems that can be used
for real-time process monitoring, predicting, and optimizing performance. If any mismatch
or anomalous data are discovered during operations without human intervention, it allows
for real-time correction. It will also facilitate the testing of new processes and materials in a
virtual environment, which reduces time and resources and eliminates physical prototypes
and the need to perform excessive amounts of experimentation.
• AI-driven automation
With contemporary AI technologies, optimizing resource usage, increasing quality
assurance, and simplifying supply chain management might be convenient and easier.
Organizing and managing large amounts of data from equipment, machinery, retailers, and
suppliers enables us to spot abnormalities and investigate data trends. It can also enable
the system to be anticipated and changed to lower disruption.
• Blockchain throughout the supply chain
Blockchain technology assures accountability, transparency, traceability, and efficiency
by providing a clear and immutable record, reducing paperwork, eliminating fraud, and
thereby accelerating transactions. Blockchain offers data security by means of robust
encryption and distributed storage, therefore lowering data tempering. It also speeds
the process and offers a single source of right information, therefore enabling manufac-
turers to exactly monitor the source and movement of products and supplies. AI and
blockchain, taken together, will enhance demand forecasting, predictive analytics, and
real-time decision-making. Blockchain technology allows one to create and manage digital
twins, virtual replicas of real-world assets, data integrity assurance, and asset management
improvement. Blockchain-as-a-service, asset tokenizing, and distributed manufacturing
networks might all give way to smart manufacturing systems.
Machines 2024, 12, 681 21 of 27

• Circular economy
By means of Industry 4.0 technology and AI integration with the concepts of circular
economy, objects can endure longer, therefore reducing the necessity for frequent replace-
ments. Manufacturers might also maximize resource use, reduce waste, and raise the
quality of their goods. Future routes in circular economy application indicated here will be
energy efficiency, material traceability, and acceptance of green manufacturing processes.
• Standardization and Development of Interoperability of Industrial Protocols in Indus-
try 4.0
The rapid progress of Industry 4.0 technologies has made it possible to integrate all
machines, raw materials, devices, and diverse systems into a single networked system.
Most old machines adhere to different protocols, which poses a barrier to their integration
and operations. Adhering to standard communication protocols and fostering interoper-
ability can effectively resolve these issues. For real-time data exchange, a real-time feedback
system and efficient machine-to-machine communication can be possible through some
of the emerging universal protocols and languages, like OPC UA (Open Platform Com-
munications Unified Architecture) and MQTT (Message Queuing Telemetry Transport).
Lastly, interoperability helps manufacturers to connect with several vendors or suppliers
with various technologies from different parts of the globe, which helps to ensure the more
recent technologies and quickly integrated and adopted and benefit the broader ecosystem.
• Preventive Maintenance Using Advanced AI
AI-driven maintenance management has already successfully overcome some of the
situations in traditional experience-based preventative or corrective maintenance. The new
system allows any user to know the current conditions and potential future failure modes
of machines or parts in the manufacturing system, which reduces machine breakdown
costs, unplanned downtime, and losses of manufacturing productivity due to accidental
failures, etc. The application of digital twin and sensor data can help to analyze data
trends in real-time by using AI and can easily detect and propose corrective action for any
failure condition before the actual event happens to cause significant disruption to the
manufacturing environment.
• Evolution of Cybersecurity from Industry 4.0 to 5.0
Cybersecurity now plays a major and more complicated role as the paradigm moves
from Industry 4.0 to Industry 5.0. Under one roof, several machines in Industry 4.0
are linked and instantly share their data from one to another. In these situations, more
connections via IoT, cloud services, and digital twin models might pose several hazards
targeted at data theft or the insertion of harmful information into the actual system. This
calls for strong defenses against industrial espionage, network invasions, and data breaches.
Under Industry 5.0, systems combine AI models, AI-driven automation, and human-
machine interfaces. Blockchain technology may help by providing immutable records,
protecting communication channels, and securely distributing data storage in the current
challenging cybersecurity management.
• Adaptation of Industry 4.0 Technologies for Small and Medium-sized Enterprises
(SMEs)
Among the several challenges SMEs must overcome to operate effectively in the
industrial environment are lack of capital investment and in-house knowledge to implement
advanced technology systems such as IoT-based devices, AI-driven automation, and smart
manufacturing solutions. One may circumvent these conditions by employing cloud-based
solutions by reducing the cost of major upfront hardware/software/know-how purchases.
Emerging IoT-based gadgets give real-time data insights that help to improve operational
efficiency. Standardized technologies enable the integration of new systems into existing
infrastructure, therefore enabling SMEs to adapt. Cooperative networks where SMEs
may share operational knowledge, technology resources, and even expertise would help
Machines 2024, 12, 681 22 of 27

the group’s development and digital transformation thereby enabling SMEs to remain
competitive by raising production efficiency. Through their adoption of these synergistic
technologies, SMEs should embrace Industry 4.0’s benefits to accelerate material processing,
maximize resource efficiency, and increase general industrial efficiency.

7. Conclusions
This study used a systematic literature review (SLR) technique to determine the current
research developments in Industry 4.0 and AI. The study included various well-known
scientific databases, like IEEE, Web of Science, Scopus, etc., under an SLR. After screening,
143 papers were chosen for review. Generally, Industry 4.0 technologies improve industrial
productivity, response times, and production efficiency, among other aspects. It is clear from
the research articles discussed in this paper that manufacturing has advanced rapidly as a
network-based manufacturing paradigm. Industries are adopting Industry 4.0’s enabling
technologies—blockchain, big data, AI, IoT, etc.—to gather and examine data during the
many phases of a product’s life. According to this literature study, Industry 4.0 and AI have
a significant impact on production efficiency and productivity across all these phases. Still,
study in this field is less than what is warranted because of a lack of combined consideration
of Industry 4.0 and AI. To help overcome these research constraints, the authors theorized
avenues of further research in several fields. This SLR highlights the following significant
contributions:
• Firstly, this paper explores the current functions and potential of Industry 4.0 technolo-
gies in various production processes across various types of raw material processing
companies. Furthermore, this work examines the impact of various Industry 4.0 tech-
nologies on all aspects of manufacturing process enhancement, which will benefit
future research:
• Secondly, this paper highlights the most significant AI technologies, which, through
a thorough application in many manufacturing subsystems, have demonstrated an
increase in their performance through data anomaly detection, process parameter and
resource optimization, and waste reduction;
• Thirdly, this study adopted the PRISMA method for an SLR and explores several
options for manufacturing efficiency improvement through the synergistic influence
of Industry 4.0 and AI technologies. The technologies of AI and Industry 4.0 and their
impacts are as follows:
1. RNN, Logistics Regressor with IoT, Cloud Computing, and Simulation have a
significant synergistic effect on early fault detection, quality control, and the
identification of anonymous data;
2. Various ML algorithms with integration of Industry 4.0 are capable of handling
real-time monitoring, prediction, and condition-based monitoring, allowing for
the visualization of warnings and errors in any production system.
Finally, this literature study identified some research challenges and concerns, as well
as future trending technologies. The study offers valuable insights to both industry leaders
and academics. Future research should scale beyond Industry 4.0 and focus on developing
a framework that integrates several Industry 5.0 technologies with AI technologies to
enhance efficiency, productivity, and sustainability in both large corporations as well as
small businesses. Moreover, blockchain and other advanced technologies, when coupled
with Industry 5.0 and AI, should greatly raise near-future production efficiency and security.

Author Contributions: Conceptualization, M.S.A. and S.P.I.; methodology, M.S.A. and S.P.I.; software,
M.S.A.; validation, M.S.A. and S.P.I.; formal analysis, M.S.A. and S.P.I.; investigation, M.S.A. and
S.P.I.; resources, F.L.; data curation, M.S.A. and S.P.I.; writing—original draft preparation, M.S.A. and
S.P.I.; writing—review and editing, M.S.A., S.P.I. and F.L.; visualization, M.S.A. and S.P.I.; supervision,
S.P.I. and F.L.; project administration, F.L.; funding acquisition, F.L. All authors have read and agreed
to the published version of the manuscript.
Machines 2024, 12, 681 23 of 27

Funding: This research was partially funded by NSF Grants CMMI 1625736 and EEC 1937128, and
the Intelligent Systems Center at Missouri University of Science and Technology.
Data Availability Statement: No new data were created.
Conflicts of Interest: Not applicable.

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