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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
manufacturing.
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
https://doi.org/10.1016/j.eng.2019.04.011
2095-8099/Ó2019 THE AUTHOR. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Engineering 5 (2019) 615–618
Contents lists available at ScienceDirect
Engineering
journal homepage: www.elsevier.com/locate/eng
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
unpredictable.
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,
Ó2018.
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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