Lakhasly

Abstract The Industrial Revolution can be termed as the transformation of traditional industrial practices into new techniques dominated by the technologies available at that time.The definition of "sustainability trilemma" a new term coined by the authors, and the reasoning for calling the next industrial revolution "Industry 4.0S" (another new term) rather than Industry 5.0 are also presented. Keywords Industrial revolution . Sustainability . Artificial Intelligence . Internet of things . Collaborative robots . Digital twins . Edge computing . Block chain . Cyber-Physical Systems Received: 18 February 2022 / Accepted: 20 January 2023 / Published online: 5 February 2023 (C) The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2023 Industry 5.0 or industry 4.0S? Introduction to industry 4.0 and a peek into the prospective industry 5.0 technologies Abirami Raja Santhi1,2 . Padmakumar Muthuswamy1,2 1 3 International Journal on Interactive Design and Manufacturing (IJIDeM) (2023) 17:947-979 by various electronic devices. Digital sensors and computers became a part of the shop floor. Although the working conditions improved tremendously during this period, exploitation of labor continued, cities became overcrowded, and widespread pollution and environmental degradation became common across the world. The ongoing fourth industrial revolution which is commonly referred to as Industry 4.0 or 4IR is built upon the third industrial revolution which relied on transistors, sensors, and micro-electronics to generate data. The term Industry 4.0 was coined by German Professor Wolfgang Wahlster, in the year 2011 at the Hannover Fair. It is the digital transformation of manufacturing industries that focuses on automation, interconnectivity, and real-time process optimization using enabling digital technologies such as Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), Cyber-Physical Systems (CPS), Cloud computing, Additive Manufacturing (AM), Digital twins, Cybersecurity and so on to communicate and control each other [4, 5]. In other words, it can be called the computerization of manufacturing in which advanced digital technologies are married to industrial machines and processes. The interconnection of these technologies into the manufacturing setup is to achieve operational efficiency, productivity, and automation to the highest possible extent [6]. This in turn creates a manufacturing ecosystem that is smart, connected, and driven by data. The fundamental model of Industry 4.0 can be divided into digital or computing technologies that are married with the systems of the physical world. While AI, ML, Big Data, Cloud Computing, and cyber security form a part of the core computing technologies, other technologies such as Automation and Robotics, IoT, CPS, and AM form the physical part. These technologies together realize the benefits of Industry 4.0 systems to enable agile, flexible, and on-demand manufacturing which is an essential part of smart manufacturing or smart factories. While there are tremendous advantages for the industries in implementing these technologies to achieve competitive advantage and higher operational efficiency, there is widespread apprehension about the potential job loss for low-skilled laborers due to the high level of automation which can cause economic imbalance and greater inequality in the society [7]. The various stages of the Industrial Revolution with their timeline, driving force, and technologies are shown in Fig. 1. While the world is still trying to adapt and realize the potential of Industry 4.0, some industrialists and scholars have started envisioning and discussing the next Industrial Revolution, Industry 5.0. If Industry 4.0 is about digitally connecting machines to enable a seamless flow of data and the highest possible optimization, Industry 5.0 is believed to bring humans back into the game for collaboration and introduce the human touch to manufactured products while simultaneously focusing on sustainable manufacturing [8, 9]. Elon Musk, a visionary entrepreneur and CEO of one of the most highly automated factories in the world, Tesla Inc., has acknowledged the downside of excessive automation through his tweet in April 2018, "Yes, excessive automation at Tesla was a mistake.Humans are underrated". He went on to admit that robots have slowed down production, and humans, not machines, were the solution. This is in line with the predictions that the next big thing would be the collaboration between humans, robots, and digital technologies. 1.1 Need for the study Despite the ongoing adaptation of Industry 4.0 in various sectors and growing discussions on Industry 5.0, this paper aims to provide a brief background on the enabling technologies of Industry 4.0 and their application in various functions of manufacturing industries, the prospective Industry 5.0 technologies and their potential applications Fig. 1 The various stages of the Industrial Revolution 1 3 948 International Journal on Interactive Design and Manufacturing (IJIDeM) (2023) 17:947-979 and attempts to answer the following five research questions (RQ), RQ1. What are the enabling technologies of Industry 4.0 and their application in manufacturing industries? RQ2. What are the socio-economic challenges of Industry 4.0 technologies? Why must the industries overlook these technologies and upgrade to the prospective Industry 5.0 technologies? RQ3. What are the prospective technologies of Industry 5.0 and their application in manufacturing industries? RQ4. What is "sustainability trilemma" and how does Industry 5.0 technologies help to overcome it?Why "Industry 5.0" must be called "Industry 4.0S"? The above questions are answered through various sections of this article. The Sect. 2 introduces the various enabling technologies of Industry 4.0 and their application in manufacturing industries which aims to answer RQ1. The socio-economic challenges of Industry 4.0 technologies and the reason to overlook these technologies and upgrade to the prospective Industry 5.0 technologies are explained in Sect. 3 which answers RQ2. The Sect. 4 of the article discusses the predictions from industry leaders and provides the definitions of Industry 5.0 as quoted by industries and scholars. The scholarly articles related to the prospective Industry 5.0 technologies and their application in manufacturing industries are discussed in Sect. 5 which answers RQ3. The definition of "sustainability trilemma" a new term coined by the authors, the sustainability dimension of Industry 5.0 technologies, and how the industries can embrace sustainable development using the technologies are discussed in Sect.1.2 Methodology and structure To understand the enabling technologies of Industry 4.0, prospective Industry 5.0 technologies, and their respective application in various functions of manufacturing industries, a comprehensive literature review was performed.The first three industrial revolutions were driven respectively by mechanization, electrification, and automation which had gradually transformed the agrarian economy into a manufacturing-based economy.To be precise, my mistake.RQ5.6 which answers RQ4.

Abstract
The Industrial Revolution can be termed as the transformation of traditional industrial practices into new techniques
dominated by the technologies available at that time. The first three industrial revolutions were driven respectively by
mechanization, electrification, and automation which had gradually transformed the agrarian economy into a manufacturing-based economy. It helped in enhancing the lifestyle of the factory workers and the healthcare system, which improved
the overall quality of living. The industries that adapted to the change witnessed a tremendous increase in the production
of goods, competitive advantage, and cross-border business opportunities. While we are currently living to see the fourth
industrial revolution (also known as Industry 4.0) unfolding around us, the world is poised for the next big leap, the fifth
industrial revolution or Industry 5.0. Hence, the first half of the paper outlines the enabling technologies of Industry 4.0
and conceptualizes how they would act as the foundation for the fifth industrial revolution. The socio-economic challenges
of the technologies and the need for Industry 5.0 technologies are also discussed. The second half of the paper outlines the
prospective technologies of Industry 5.0, their potential applications from the perspective of industry leaders and scholars
and conceptualizes how they can overcome the challenges of Industry 4.0. The definition of “sustainability trilemma” a
new term coined by the authors, and the reasoning for calling the next industrial revolution “Industry 4.0S” (another new
term) rather than Industry 5.0 are also presented.
Keywords Industrial revolution · Sustainability · Artificial Intelligence · Internet of things · Collaborative robots ·
Digital twins · Edge computing · Block chain · Cyber-Physical Systems
Received: 18 February 2022 / Accepted: 20 January 2023 / Published online: 5 February 2023
© The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2023
Industry 5.0 or industry 4.0S? Introduction to industry 4.0 and a peek
into the prospective industry 5.0 technologies
Abirami Raja Santhi1,2 · Padmakumar Muthuswamy1,2
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM) (2023) 17:947–979
by various electronic devices. Digital sensors and computers became a part of the shop floor. Although the working conditions improved tremendously during this period,
exploitation of labor continued, cities became overcrowded,
and widespread pollution and environmental degradation
became common across the world. The ongoing fourth industrial revolution which is commonly referred to as Industry
4.0 or 4IR is built upon the third industrial revolution which
relied on transistors, sensors, and micro-electronics to generate data. The term Industry 4.0 was coined by German
Professor Wolfgang Wahlster, in the year 2011 at the Hannover Fair. It is the digital transformation of manufacturing
industries that focuses on automation, interconnectivity, and
real-time process optimization using enabling digital technologies such as Internet of Things (IoT), Machine Learning
(ML), Artificial Intelligence (AI), Cyber-Physical Systems
(CPS), Cloud computing, Additive Manufacturing (AM),
Digital twins, Cybersecurity and so on to communicate and
control each other [4, 5]. In other words, it can be called
the computerization of manufacturing in which advanced
digital technologies are married to industrial machines and
processes. The interconnection of these technologies into
the manufacturing setup is to achieve operational efficiency,
productivity, and automation to the highest possible extent
[6]. This in turn creates a manufacturing ecosystem that is
smart, connected, and driven by data.
The fundamental model of Industry 4.0 can be divided
into digital or computing technologies that are married
with the systems of the physical world. While AI, ML, Big
Data, Cloud Computing, and cyber security form a part of
the core computing technologies, other technologies such
as Automation and Robotics, IoT, CPS, and AM form the
physical part. These technologies together realize the benefits of Industry 4.0 systems to enable agile, flexible, and
on-demand manufacturing which is an essential part of
smart manufacturing or smart factories. While there are
tremendous advantages for the industries in implementing
these technologies to achieve competitive advantage and
higher operational efficiency, there is widespread apprehension about the potential job loss for low-skilled laborers due
to the high level of automation which can cause economic
imbalance and greater inequality in the society [7]. The various stages of the Industrial Revolution with their timeline,
driving force, and technologies are shown in Fig. 1.
While the world is still trying to adapt and realize the
potential of Industry 4.0, some industrialists and scholars
have started envisioning and discussing the next Industrial
Revolution, Industry 5.0. If Industry 4.0 is about digitally
connecting machines to enable a seamless flow of data and
the highest possible optimization, Industry 5.0 is believed
to bring humans back into the game for collaboration and
introduce the human touch to manufactured products while
simultaneously focusing on sustainable manufacturing [8,
9]. Elon Musk, a visionary entrepreneur and CEO of one
of the most highly automated factories in the world, Tesla
Inc., has acknowledged the downside of excessive automation through his tweet in April 2018, “Yes, excessive automation at Tesla was a mistake. To be precise, my mistake.
Humans are underrated”. He went on to admit that robots
have slowed down production, and humans, not machines,
were the solution. This is in line with the predictions that the
next big thing would be the collaboration between humans,
robots, and digital technologies.
1.1 Need for the study
Despite the ongoing adaptation of Industry 4.0 in various sectors and growing discussions on Industry 5.0, this
paper aims to provide a brief background on the enabling
technologies of Industry 4.0 and their application in various functions of manufacturing industries, the prospective
Industry 5.0 technologies and their potential applications
Fig. 1 The various stages of the Industrial Revolution
1 3
948
International Journal on Interactive Design and Manufacturing (IJIDeM) (2023) 17:947–979
and attempts to answer the following five research questions (RQ),
RQ1. What are the enabling technologies of Industry 4.0
and their application in manufacturing industries?
RQ2. What are the socio-economic challenges of Industry 4.0 technologies? Why must the industries overlook
these technologies and upgrade to the prospective Industry
5.0 technologies?
RQ3. What are the prospective technologies of Industry
5.0 and their application in manufacturing industries?
RQ4. What is “sustainability trilemma” and how does
Industry 5.0 technologies help to overcome it?
RQ5. Why “Industry 5.0” must be called “Industry
4.0S”?
The above questions are answered through various sections of this article. The Sect. 2 introduces the various
enabling technologies of Industry 4.0 and their application
in manufacturing industries which aims to answer RQ1. The
socio-economic challenges of Industry 4.0 technologies and
the reason to overlook these technologies and upgrade to
the prospective Industry 5.0 technologies are explained in
Sect. 3 which answers RQ2. The Sect. 4 of the article discusses the predictions from industry leaders and provides
the definitions of Industry 5.0 as quoted by industries and
scholars. The scholarly articles related to the prospective
Industry 5.0 technologies and their application in manufacturing industries are discussed in Sect. 5 which answers
RQ3. The definition of “sustainability trilemma” a new
term coined by the authors, the sustainability dimension
of Industry 5.0 technologies, and how the industries can
embrace sustainable development using the technologies
are discussed in Sect. 6 which answers RQ4. The key difference between Industry 4.0 and Industry 5.0 technologies
in terms of sustainable development (economic, social, and
environmental sustainability) forms the core ideology of the
paper. The article concludes with the justifications for calling the next industrial revolution “Industry 4.0S” rather than
“Industry 5.0” which answers RQ5.
1.2 Methodology and structure
To understand the enabling technologies of Industry 4.0,
prospective Industry 5.0 technologies, and their respective
application in various functions of manufacturing industries, a comprehensive literature review was performed.
The various search terms combinations such as “Industry 4.0”, “Industry 5.0" and various technologies such as
Artificial Intelligence, Machine Learning, Digital twins,
and so on were used to find the relevant articles in Google
Scholar, Scopus, and Web of Science. A preliminary screening of the articles to identify their relevance to the study
was conducted by reading the title and abstract. The main
findings and conclusions are summarized in a narrative or
descriptive format. It should be noted that the study does not
include all the articles published on the topic but is focused
only to cover the latest and important progress in the area,
to summarize and provide an overview to the readers on the
status and future direction. The article follows a descriptive
review approach, and the overall structure of the paper is
shown in Fig. 2.
2 Enabling technologies of industry 4.0
2.1 Artificial intelligence (AI)
Artificial Intelligence (AI) is an algorithm-based intelligence fed to machines to impart problem-solving, decisionmaking skills, and perform human-like assignments [229].
In other words, AI make computers think and behave like
humans. It is a combination of several digital and software
technologies that acts as the driving force of Industry 4.0.
The origin of AI can be traced back to the 1940s [10] and
took the first big leap in the 1980s [11]. The subsequent
inventions such as statistical learning [12], Greedy learning algorithm [13], Recurrent Neural Network [14], Graph
Transformer Network [15], Deep Belief Network [16], and
Convolutional Neural Network [17] paved the way for various new algorithms that are currently been used and are
being developed on a regular interval.
The application of AI in Industry 4.0 has shown great
potential in predictive maintenance, predictive analytics,
inventory management, machine vision, industrial robotics,
and supply chain management [231]. Liu et al. [18] have
reviewed the application of various AI-driven algorithms
such as k-NN, Naive Bayes, ANN, and Deep Learning
in fault diagnostics of rotating machinery that helped in
reducing the machine downtime, cost of maintenance, and
eliminating safety threats. As each of the algorithms have
their strengths and limitations such as accuracy, speed, and
robustness, they propose to develop a hybrid intelligent system to address future challenges.
Zhao et al. [19] have used Recurrent Neural Networks
(RNN) based algorithm as a predictive maintenance tool to
monitor the health of the machine and successfully applied
the technique to predict tool wear in a milling operation, and
fault diagnosis in gearbox and bearings. Wang et al. [20]
have applied Deep Belief Network (DBN), a data-driven
technique to build an accurate relationship between various
operational parameters used in a polishing operation and the
amount of material removed. The same technique was also
used by Deutsch [21] to predict the useful life of a hybrid
ceramic bearing. As the various operations of manufacturing industries are nonlinear, stochastic, and have a lot of
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International Journal on Interactive Design and Manufacturing (IJIDeM) (2023) 17:947–979
Fig. 3 Opportunities of AI in a manufacturing system
Fig. 2 The structure of the article
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International Journal on Interactive Design and Manufacturing (IJIDeM) (2023) 17:947–979
neuron to mimic the human brain. Machine learning methods can be broadly classified into supervised and unsupervised learning, although few literatures show two more
categories, semi-supervised and reinforcement learning.
The classifications of ML and a few common algorithms are
shown in Fig. 4.
ML allows the connected systems to transfer data between
them and improve the process on its own based on algorithms. The repeated learning and optimization loop lead
to unprecedented performance that converts a traditional
automated factory into a smart factory. Condition monitoring of machinery, health monitoring of structures, predictive
maintenance, predictive quality control, and supply chain
management are some of the applications of ML in the
manufacturing industry [231]. The data collected through
various sensors are processed using machine learning algorithms to recognize the pattern of failure and even predict
future failures which can eliminate the routine manual
inspections. By combining machine vision with ML algorithms, real-time monitoring and identification of defective
uncertainties many researchers have tried different AI-based
techniques to enable the optimal material flow. The potential opportunities of artificial intelligence in the manufacturing industry are shown in Fig. 3.
2.2 Machine learning (ML)
Machine learning is a subset of artificial intelligence that
uses data, algorithms, and software to predict the outcome
accurately through statistical learning. The historical data is
used to train the system to make predictions and improve
gradually to increase the prediction accuracy. Although
machine learning, deep learning (DL), and neural network
or artificial neural network (ANN) are all a subset of artificial intelligence, IBM classifies DL as a branch of ML, and
ANN as a branch of DL. While traditional machine learning is dependent on human teaching to learn, deep learning
can do it automatically. ANN has an input node layer, output node layer, and numerous intermediate hidden layers.
Each node that is connected to the other acts as an artificial
Fig. 4 Supervised and unsupervised machine learning algori