By
F
Expert Systems for
Engineering Applications
Tan Chee Nian,
Ir. Dr Tan Chee Fai,
Dr Low Cheng Yee,
Ir. Dr Leong Wai Yie
rom the first industrial revolution till the latest
fourth industrial revolution, from the use of
steam and water to generate force in the
olden days to today’s cyber-physical systems, it
has been proven that digitalisation has become
more prominent in our daily life. The fourth
industrial revolution, which includes developments
in artificial intelligence and machine learning,
rapid prototyping, and genetics and biotechnology
will require dramatic changes of skills sets.
According to the World Economic Forum 2016,
two million jobs will be created by digital industrial
and service sectors with a loss of seven million
jobs in traditional industrial and service sectors.
Industry 4.0 also focuses on digital technologies
like sensors or connectivity devices, and smart
applications for manufacturing execution systems.
Artificial intelligence (AI) which is closely related
to cyber-physical systems is playing a paramount
role in the multistage development of Industry 4.0.
There are many types of AI in the current market,
for instance, expert systems, artificial neural
networks and fuzzy systems.
Expert systems, defined as computer systems
that emulate the decision-making ability of
a human expert, were among the first truly
successful forms of artificial intelligence software.
An expert system is defined as a programme
that tries to mimic human expertise by applying
the inference method to a particular body of
knowledge (Mansyur et al., 2013). This system is
able to computerise expert knowledge in a specific
subject domain in a short time and provide users
with easily accessible solutions in a useful and
practical way.
Expert knowledge is a combination of the
theoretical understanding of a problem and
a collection of heuristic problem-solving rules
that experience has shown to be effective in the
domain. Constructing expert systems involves
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