Ron McGary wrote a paper on the future of artificial intelligence in manufacturing. He discusses how AI can reduce costs by enabling predictive maintenance to minimize unexpected downtime. Wireless monitoring technologies allow AI systems to continuously monitor equipment and predict failures. Expert systems and knowledge-based AI can be applied to manufacturing processes to optimize production parameters and diagnose faults. The basic architecture of AI systems involves continuous data collection, analysis, and feedback to adjust processes accordingly. As AI technologies continue to advance, they will play a larger role in improving manufacturing productivity and competitiveness.

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Ron McGary
Professor Wayne
Management 4215
28 June 2012
The Future of Artificial Intelligence in Manufacturing
Have you ever wondered about the future of manufacturing with the advanced technologies
available today? Have you ever heard of a manufacturing plant working practically by itself,
producing millions of parts a day? The Lego’s manufacturing plant and others around the world
are advancing their technologies to reap the benefits of artificial intelligence to reduce
manufacturing costs and keeping the human intervention at a minimum.
In 2009 reactive maintenance cost the United States $750 billion (reliabilityweb). Reactive
maintenance is repairing manufacturing machines during production run hours. This is also
called “unplanned downtime”. This cost is part of the reason why America is not a real
competitor in 21st century manufacturing. The good news is American manufacturing is waking
up to the tune of implementing new technologies to predict the failure of a piece of production
machinery before it fails and not during the production run. New developments in wireless data
collection technologies are lowering the costs of gathering the data necessary to perform
predictive maintenance for a broader range of equipment (Moore). Wireless-based continuous,
automatic monitoring by the CMMS (Computer Maintenance Management Software) can extend
condition-based maintenance strategies to more equipment as well as improves the performance

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of corrective maintenance. There are a number of wireless technologies that are available for
continuously monitoring equipment condition. Cellular telephone networks are being used to
monitor equipment, particularly in remote locations or when the equipment is frequently mobile.
Equipment is even being monitored over satellite communication networks for those areas
beyond cellular coverage (Stargardt). Artificial intelligence will not only tell us when the
machine will break down but, how often it should be maintained to keep the system reliable.
These AI systems are made up of knowledge-based systems which are expert systems to
improve the process. The expert system is a major application of AI today. Also known as
knowledge-based systems, expert systems act as intelligent assistants to human experts or serve
as a resource to people who may not have access to an expert. The major difference between an
expert system and a simple database containing information on a particular subject is that the
database can only give the user discrete facts about the subject, whereas an expert system uses
reasoning to draw conclusions from stored information. The purpose of this AI application is not
to replace our human experts, but to make their knowledge and experience more widely
available. Some of the common applications are flexible Manufacturing Systems (FMS).
The user’s concept of the flexible manufacturing system (FMS) will determine the control
philosophy of the future. The future will almost certainly continue to cast programmable
controllers as an important player in the factory. Control strategies will be distributed with
“intelligence” instead of being centralized. Super PLCs (Programmable Controllers) will be used
in applications requiring complex calculations, network communication, and supervision of
smaller PLCs and machine controllers (Bryan). Once an error is detected, it can be interpreted
using statistical analysis. This type of statistical data analysis is, in fact, part of the foundation of
artificial intelligence systems. These systems continuously collect data about a process and

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adjust production parameters accordingly. They then store their data measurements in a global
database for use in later statistical analysis. A knowledge AI system is, in reality, an enhanced
diagnostic system. Knowledge systems not only detect faults and process behaviors based on
resident knowledge, but also make decisions about the process and/or the probable cause of a
fault. In a batching system a knowledge system would go beyond just diagnosing the fault. It
would also provide suggestions about probable faulty devices, as well as make a decision about
whether to continue the process (if the fault is noncritical) or to shut down (if the fault is critical).
The system bases these decisions on its programmed knowledge and a set of rules that defines
each fault condition. Since human experts will not be replaced by these systems, there are basic
architectures that drive each layer of communication.
The basic architecture of AI is not that complex (Fig1. ONLINEMCA). It is the internal details
of a system that provides the feedback for a fully-functional AI system.

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Figure 1
Artificial Intelligence is a tool that the United States can and will improve on now and in the
future. As manufacturing becomes more demanding and complex more systems will be put in
place to accommodate manufacturing. As maintenace activities decrease with more intelligence
built in the more product can be produced. Condition monitoring of machines are being
developed into more predictable technologies that will tell the critical point to apply the effort.
Putting all the technologies together for long term predictions of what is coming in the future of
AI is as critical as surviving in the 21st century manufacturing.

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Work Cited
Bryan, L.E.. Programmable Controllers, Theory and Implementation. Atlanta. Industrial Text
and Video. 1997. Print.
“Building on the Lego Legacy”. Modular solutions in manufacturing can take their cue from
Lego constructions and more advanced robotic designs and applications. 2010. Web. Robert
Malone. June 24, 2012.
“Maintenance and Share Price—Mutually Dependent” Making Common Sense Common
Practice, Models for Manufacturing Excellence . 2009. Web. Ron Moore PE. 24 June 2012.
ONLINEMCA.COM. 2009. Web. 24 June 2012.
A fascinating look at a LEGO manufacturing facility. Reliable Plant.
http://www.reliableplant.com/View/27712/Look-LEGO-manufacturing-facility, Web. 24 June
2012.
Stargardt, Wayne. Condition-Based Maintenance Using Wireless Monitoring: Developments and
Examples by, Aleier, Inc. www.reliabilityweb.com. 2006. 24 June 2012.