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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.