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From Intelligence Science to Intelligent Manufacturing

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The aim of intelligent manufacturing is to establish flexible and adaptive manufacturing operations locally or globally by using integrated information technology (IT) and artificial intelligence (AI) that can combine advanced computing power with manufacturing equipment. Intelligent manufacturing depends on the timely acquisition, distribution, and utilization of real-time data from both machines and processes on manufacturing shop floors and even across product life-cycles. Effective information sharing can improve production quality, reliability, resource efficiency, and the recyclability of end-of-life products. Intelligent manufacturing built on digitalization is also intended to be more sustainable and to contribute to the factories of the future. However, intelligent manufacturing depends extensively on AI. To better grasp the future of intelligent manufacturing, it is necessary to understand AI. This paper provides the author’s perspective from intelligence science to intelligent manufacturing.
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From Intelligence Science to Intelligent Manufacturing
Lihui Wang
KTH Royal Institute of Technology, Stockholm 10044, Sweden
1. Introduction
The aim of intelligent manufacturing is to establish flexible and
adaptive manufacturing operations locally or globally by using
integrated information technology (IT) and artificial intelligence
(AI) that can combine advanced computing power with manufac-
turing equipment. Intelligent manufacturing depends on the
timely acquisition, distribution, and utilization of real-time data
from both machines and processes on manufacturing shop floors
[1] and even across product life-cycles. Effective information shar-
ing can improve production quality, reliability, resource efficiency,
and the recyclability of end-of-life products. Intelligent manufac-
turing built on digitalization is also intended to be more sustain-
able and to contribute to the factories of the future. However,
intelligent manufacturing depends extensively on AI. To better
grasp the future of intelligent manufacturing, it is necessary to
understand AI. This paper provides the author’s perspective on AI
from intelligence science to intelligent manufacturing.
2. A brief history of AI
AI is a branch of intelligence science. The field of intelligence
science broadly covers two areas: natural intelligence and artificial
intelligence. Natural intelligence is the science of discovering the
intelligent behaviors of living systems, while artificial intelligence,
or AI, is both the science and the engineering of making intelligent
software systems and machines. These two research areas have con-
tributed to each other over decades. Advancements in natural intel-
ligence have laid a solid foundation for AI research on artificial
neural networks (ANNs), genetic algorithms (GAs), ant colony opti-
mization (ACO), etc., while advanced AI tools have helped to speed
up discoveries in natural intelligence [2]. Because of the relatively
short history of AI, research in this field is still active, promising,
and yet to be discovered further, such as in the context of
Before discussing intelligent manufacturing, it is necessary to
briefly review the history of AI, as summarized in Fig. 1. The history
of AI can be traced back to the early 1940s. The first AI was a binary
ANN model created by Warren McCulloch and Walter Pitts of the
University of Illinois in 1943 [3]. Although their model only consid-
ered the binary state (i.e., on/off for each neuron), it served as a
basis for rapid ANN research in the late 1980s. In 1950, British
mathematician Alan Turing proposed the well-known Turing Test
[4] to determine whether machines can think. The Turing Test is
performed through computer communication involving one
examiner, one human, and one machine (i.e., computer) in separate
rooms. The examiner can ask any questions. If the examiner cannot
distinguish the machine from the human on the basis of their
answers, the machine passes the test. In 1951, Marvin Minsky
and Dean Edmonds, two graduate students from Princeton Univer-
sity, built the first neuron computer to simulate a network of 40
neurons [5].
An important milestone in AI development was the first AI
workshop [6], which was held in 1956 at Dartmouth College by
John McCarthy. This workshop marked the end of the ‘‘dark age”
and the beginning of ‘‘the rise of AI” in AI history. The term ‘‘artifi-
cial intelligence”, suggested by McCarthy, was agreed upon at that
time and is still in use. McCarthy later moved to Massachusetts
Institute of Technology (MIT); in 1958, he defined the first AI lan-
guage, LISP, which is still used today. One of the most ambitious
projects in this area was the General Problem Solver (GPS) [7],
which was created in 1961 by Allen Newell and Herbert Simon
of Carnegie Mellon University. The GPS is based on formal logic
and can generate an infinite number of operators attempting to
find a solution; however, it is inefficient in solving complicated
problems. In 1965, Lotfi Zadeh of UC Berkeley published his famous
paper ‘‘Fuzzy sets” [8], which is the foundation of fuzzy set theory.
The first expert system, DENDRAL [9], was developed at Stanford
University in 1969 in a project that was funded by the National
Aeronautics and Space Administration (NASA) and led by Joshua
Lederberg, a Nobel Prize laureate in genetics. At that time, how-
ever, because most AI projects could only handle toy problems
rather than real-world ones, many projects were canceled in the
United States, United Kingdom, and several other countries. AI
research entered into a so-called ‘‘AI winter.”
Despite these funding cuts, AI research continued. In 1969,
Bryson and Ho [10] proposed the basis of back-propagation for
neural network learning. Furthermore, the first GA was proposed
in 1975 by John Holland of the University of Michigan, who used
selection, crossover, and mutation as genetic operators for
optimization [11]. In 1976, MYCIN [12] was developed by the same
group as DENDRAL at Stanford University. MYCIN, which is a rule-
based expert system for blood disease diagnosis using 450 if-then
rules, was found to perform better than a junior doctor.
After 30 years, work on neural networks was taken up again in
the field of AI. A new period—in which AI became a science—began
in 1982, when John Hopfield published his Hopfield networks [13],
which remain popular today. In 1986, back-propagation became a
2095-8099/Ó2019 THE AUTHOR. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.
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Engineering 5 (2019) 615–618
Contents lists available at ScienceDirect
journal homepage:
real implemented learning algorithm [14] in ANN, 16 years after it
was proposed. It also triggered the start of distributed AI (DAI)
through parallel distributed processing. After 22 years, fuzzy set
theory or fuzzy logic was successfully built into dishwashers and
washing machines in 1987 by Japanese companies. In 1992,
genetic programming [15] was proposed by John Koza to manipu-
late a symbolic code representing LISP programs. Based on the
ideas of DAI and artificial life, intelligent agents gradually took
shape in the mid-1990s. In the late 1990s, hybrid systems of fuzzy
logic, ANN, and GA became popular for solving complex problems.
More recently, various new AI approaches have come into being,
including ACO, particle swamp optimization (PSO), artificial
immune optimization (AIO), and DNA computing. The potential
of AI in the future—such as in manufacturing—remains
The first popular AI tool was probably the AI-based chess-
playing computer program Deep Blue [16], which was created by
International Business Machines Corporation (IBM). When Garry
Kasparov, the world chess champion at that time, played with
Deep Blue in 1997 in an exhibition match, he lost the match to
Deep Blue by 2.5 to 3.5. Another early example is the Honda ASIMO
robot in 2005, which was able to climb stairs. For a robot to move
in an unstructured environment and be commanded by a human, it
requires the abilities of natural language processing, computer
vision, perception, object recognition, machine learning, and
motion control at runtime. More recently, in 2016, AlphaGo [17]
of DeepMind beat the world Go champion Lee Sedol in four out
of five games using cloud computing, reinforcement leaning, and
a Monte Carlo search algorithm combined with a deep neural net-
work for decision-making. Its newer version, AlphaGo Zero [18],
surpassed the ability of AlphaGo in just three days through self-
learning from scratch. Today, AI techniques and systems can be
found in every field from chess playing to robot control, disease
diagnosis to airplane autopilots, and smart design to intelligent
manufacturing. In addition to the AI techniques summarized in
Fig. 1, machine learning and deep learning show a great deal of
promise for intelligent manufacturing.
Table 1 classifies typical machine learning models based on
whether they are supervised or unsupervised, discriminative or
generative, and deep learning or non-deep learning.
3. Representative examples of AI in manufacturing
In the context of manufacturing, intelligence science—or, more
specifically, AI in the form of machine learning models—
contributes to intelligent manufacturing. Fig. 2 depicts one
scenario of human–robot collaboration (HRC) in which data from
sensors and field devices are transformed to knowledge after the
application of appropriate machine learning models [19].
Knowledge is further transformed into actions using domain-specific
HRC decision modules. Consequently, human operators can work
with robots safely in an immersive environment, while the robots
Fig. 1. A brief history of AI.
Table 1
Typical machine learning models.
Machine learning models Supervised/unsupervised/semi-supervised Discriminative/generative Deep learning/non-deep learning
K-means clustering Unsupervised Generative Non-deep learning
K-nearest neighbors Supervised Discriminative Non-deep learning
Support vector machine Supervised Discriminative Non-deep learning
Hidden Markov model Supervised Discriminative Non-deep learning
Random forest Supervised Discriminative Non-deep learning
XGBoost Supervised Discriminative Non-deep learning
Ensemble methods Supervised Discriminative Non-deep learning
Convolutional neural network Supervised Discriminative Deep learning
Recurrent neural network Supervised Discriminative Deep learning
Long short-term memory Supervised Discriminative Deep learning
Naive Bayes Supervised Generative Non-deep learning
Gaussian mixture model Supervised Generative Non-deep learning
Generative adversarial nets Semi-supervised Generative Deep learning
616 L. Wang / Engineering 5 (2019) 615–618
can predict what the humans will do next and provide in situ assis-
tance as needed [20,21].
Brain robotics [22] is another example of adaptive robot control
using the brainwaves of experienced human operators. Rather than
following the data–knowledge–action chain, a brainwave–action
progression can be realized by mapping human brainwave pat-
terns to robot control commands through proper training, as
shown in Fig. 3. A 14-channel EMOTIV EPOC
device (EMOTIV,
USA) is used in this case to collect human brainwave signals. The
matching commands after signal processing are then passed on
to the robot controller for adaptive execution.
4. Opportunities and challenges
Enabled by AI and the latest IT technologies such as cloud com-
puting, big data analytics, the Internet of Things (IoT), and mobile
Internet/5G, numerous opportunities for intelligent manufacturing
lie ahead. These new technologies will facilitate real-time informa-
tion sharing, knowledge discovery, and informed decision-making
in intelligent manufacturing, as follows:
The IoT provides better connectivity of machines and field
devices for data collection, thereby making real-time data
collection possible.
Mobile Internet/5G makes it practical to transmit a large
amount of data in ultra-low latency for real-time informa-
tion sharing.
Cloud computing offers rapid and on-demand data analysis;
it also helps store data, which can be easily shared with
authorized users.
Big data analytics can reveal hidden patterns and meaningful
information in data so as to convert data into information
and further transform information into knowledge.
For example, new opportunities in intelligent manufacturing
may include: remote real-time monitoring and control with lit-
tle delay, defect-free machining by means of opportunistic pro-
cess planning and scheduling, cost-effective and secure
predictive maintenance of assets, and holistic planning and con-
trol of complex supply chains. Moreover, intelligent manufacturing
in the near future will benefit from the aforementioned technolo-
gies in different temporal scales, as follows:
Better horizontal and vertical integrations in five years may
remove the gaps between automation islands by 80% in gen-
eral, mainly enabled by the IoT and mobile Internet.
Fig. 2. Machine learning in intelligent manufacturing.
Fig. 3. Brain robotics HRC. Reproduced from Ref. [22] with permission of Elsevier,
L. Wang / Engineering 5 (2019) 615–618 617
In ten years, experience-driven manufacturing operations
may become data-driven with prior knowledge support,
mainly enabled by cloud computing and big data analytics.
In 20 years, numerous small and medium-sized enterprises
(SMEs) may gain a competitive edge in the global market
by being powered by cloud manufacturing and made avail-
able to all.
Nevertheless, complexity and uncertainty will remain major
challenges in manufacturing in the years to come. AI and machine
learning can provide opportunities to relax or even resolve these
challenges to a large extent. For example, deep learning can be used
to better understand the manufacturing context and more accu-
rately predict a future problem or failure in a manufacturing pro-
cess before it happens, thus leading to defect-free manufacturing.
Safe HRC is another challenge in the progression toward intelli-
gent and flexible automation that includes humans in the loop. Such
collaboration is useful and necessary, especially in manufacturing
assembly operations, where deep learning can help make robots
intelligent enough to assist human operators while providing
much-improved context awareness toward absolute human safety.
Finally, cybersecurity and new business models must be ade-
quately addressed before intelligent manufacturing can be put into
practice in the factories of the future.
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Purpose During the past decade, the necessity to integrate manufacturing and sustainability has increased mainly to reduce the adverse effect on the manufacturing industry, transforming traditional manufacturing into smart manufacturing by adopting the latest manufacturing technology as part of the Industry 4.0 revolution. Smart manufacturing has piqued the interest of both academics and industry. Manufacturing is a foundation of products and services required for human health, safety, and well-being in modern society and from an organizational standpoint. This paper uses bibliometric analysis better to understand the relationship between smart manufacturing and sustainability scholarship and provide an up-to-date account of current industry practices. Design/methodology/approach This paper used the bibliometric analysis method to analyze and draw conclusions from 839 articles retrieved from the Scopus database from 1994 to February 2022. The methodology is divided into four steps: data collection, analysis, visualization, and interpretation. The current study aims to comprehend smart manufacturing and sustainability scholarship using the bibliometric R-package and VOSviewer software. Findings The study provides fascinating insights that may assist scholars, industry professionals, and top management in conceptualizing smart manufacturing and sustainability in their organizations. The results show that the number of publications has significantly increased from 2015 onwards, reaching a maximum of 317 journals in 2021 with an increasing publication annual growth rate of 21.9%. The United Kingdom, India, the United States of America, Italy, France, Brazil and China were the most productive countries in terms of the total number of publications. Technological Forecasting and Social Change, Journal of Cleaner Production, International Journal of Production Research, Production Planning and Control, Business Strategy and the Environment Technology in Society, and Benchmarking: An International Journal emerged as the top outlets. Research limitations/implications The research in the area of smart manufacturing and sustainability is underpinned by this study, which aims to understand the trends in this field over the last two decades in terms of prolific authors, most influential journals, key themes, and the field's intellectual and social structure. However, according to the research, this field is still in its early stages of development. As a result, a more in-depth analysis is required to aid in the development of a better understanding of this new field. Originality/value The paper focuses on integrating smart manufacturing and sustainability through increased interest from 2015 onwards through the literature review. Specific policies should be formulated to improve the manufacturing sector's competence. Furthermore, these findings can guide researchers who want to delve deeper into smart manufacturing and sustainability.
Anomaly detection of machine tools plays a vital role in the machinery industry to sustain efficient operation and avoid catastrophic failures. Compared to traditional machine learning and signal processing methods, deep learning has greater adaptive capability and end-to-end convenience. However, challenges still exist in recent research in anomaly detection of machine tools based on deep learning despite the marvelous endeavors so far, such as the necessity of labeled data for model training and insufficient consideration of noise effects. During machine operation, labeled data is often difficult to obtain; the collected data contains varying degrees of noise disturbances. To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. A parallel convolutional distribution fitting (PCDF) module is constructed, which can effectively fuse multi-sensor information and enhance network robustness by training in parallel to better fit the data distribution with unsupervised learning. A fused directional distance (FDD) loss function is designed to comprehensively consider the distance and angle differences among the data, which can effectively suppress the influence of noises and further improve the model robustness. The proposed method is validated by real computer numerical control (CNC) machine tool data, obtaining better performance of unsupervised anomaly detection under different noises compared to other popular unsupervised improved autoencoder methods.
Industry 4.0 insists companies to apply intelligent application technology to their industries. Machine learning as one of field of Artificial Intelligence has used widely in Smart Factory, such as to detect product defects, to predict potential problems and for its solutions. PT. Yokogawa Indonesia, one of global company, wanted to prepare its employees to implement Smart Factory, as its response for Industry 4.0 and competition with other companies. As a solution to this problem, the community service held machine learning training using Python for PT. Yokogawa Indonesia’s employees. The training was held once a week for five weeks. Interaction and discussion online between trainer and participants used Teams Microsoft application. It also used google classroom for managing materials and assignments during this training. More than 50% of participants never learn machine learning before this training. In the last session of the training, questionnaire was given to the participants. As the result, a half of total of participants agreed that their knowledge about machine learning has increased significantly through this training.
Innovation and transformative changes in products, manufacturing technologies, business strategies, and manufacturing paradigms have profoundly changed the manufacturing systems. In addition to being environmentally, economically socially sustainable, manufacturing systems are increasingly using intelligent technologies to be even more resilient, responsive, and adaptable. A new Adaptive Cognitive Manufacturing Systems (ACMS) paradigm, its drivers, enablers, and characteristics, including cognitive adaptation, is presented. Classification and definitions of four types of adaptability in manufacturing systems are included. Human-centric collaboration of workers and intelligent machines and applications, and the future of work in cognitive adaptive manufacturing systems are outlined. Cognitive Digital Twins (CDT), their features, evolution, and their use to support humans in intelligent, collaborative manufacturing settings are discussed. Industrial applications and case studies are used to illustrate the presented concepts and paradigms. Challenges and future research directions to achieve the ACMS paradigm and implement more intelligent, more adaptive, and sustainable manufacturing systems are presented. The presented novel concepts and technologies make significant contributions to the fast-evolving field of manufacturing systems. This pioneering research sheds light on many important future research topics and provides a road map and motivation for researchers in this field.
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Intuitive and robust multimodal robot control is the key towards human-robot collaboration (HRC) for manufacturing systems. Multimodal robot control methods were introduced in previous studies. The methods allow human operators to control robot intuitively without programming brand-specific code. However, most of the multimodal robot control methods are unreliable because the feature representations are not shared across multiple modalities. To target this problem, a deep learning-based multimodal fusion architecture is proposed in this study for robust multimodal HRC manufacturing systems. The proposed architecture consists of three modalities: speech command, hand motion, and body motion. Three unimodal models are first trained to extract features, which are further fused for representation sharing. Experiments show that the proposed multimodal fusion model outperforms the three unimodal models. The study indicates a great potential to apply the proposed multimodal fusion architecture to robust HRC manufacturing systems.
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This paper introduces an approach to controlling an industrial robot using human brainwaves as a means of communication. The developed approach starts by establishing a set of training sessions where an operator is enquired to think about a set of defined commands for the robot and record the brain activities accordingly. The results of the training sessions are then used on the shop floor to translate the brain activities to a set of robot control commands. An industrial case study is carried out to assist the operator in coordinating a collaborative assembly task of a car engine manifold.
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A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
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In human-robot collaborative manufacturing, industrial robots would work alongside human workers who jointly perform the assigned tasks seamlessly. A human-robot collaborative manufacturing system is more customised and flexible than conventional manufacturing systems. In the area of assembly, a practical human-robot collaborative assembly system should be able to predict a human worker’s intention and assist human during assembly operations. In response to the requirement, this research proposes a new human-robot collaborative system design. The primary focus of the paper is to model product assembly tasks as a sequence of human motions. Existing human motion recognition techniques are applied to recognise the human motions. Hidden Markov model is used in the motion sequence to generate a motion transition probability matrix. Based on the result, human motion prediction becomes possible. The predicted human motions are evaluated and applied in task-level human-robot collaborative assembly.
Timely context awareness is key to improving operation efficiency and safety in human-robot collaboration (HRC) for intelligent manufacturing. Visual observation of human workers’ motion provides informative clues about the specific tasks to be performed, thus can be explored for establishing accurate and reliable context awareness. Towards this goal, this paper investigates deep learning as a data driven technique for continuous human motion analysis and future HRC needs prediction, leading to improved robot planning and control in accomplishing a shared task. A case study in engine assembly is carried out to validate the feasibility of the proposed method.
Computational properties of use to biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
The disciplines of artificial intelligence and artificial life build computational systems inspired by various aspects of life. Despite the fact that living systems are composed only of non-living atoms there seems to be limits in the current levels of understanding within these disciplines in what is necessary to bridge the gap between non-living and living matter.
Cloud manufacturing as a trend of future manufacturing would provide cost-effective, flexible and scalable solutions to companies by sharing manufacturing resources as services with lower support and maintenance costs. Targeting the Cloud manufacturing, the objective of this research is to develop an Internet- and Web-based service-oriented system for machine availability monitoring and process planning. Particularly, this paper proposes a tiered system architecture and introduces IEC 61499 function blocks for prototype implementation. By connecting to a Wise-ShopFloor framework, it enables real-time machine availability and execution status monitoring during metal-cutting operations, both locally or remotely. The closed-loop information flow makes process planning and monitoring feasible services for the Cloud manufacturing.