Intelligent Computation for Manufacturing

Intelligent Computation for Manufacturing

Ashraf Afify (King Saud University, Saudi Arabia & Zagazig University, Egypt)
Copyright: © 2013 |Pages: 36
DOI: 10.4018/978-1-4666-4034-4.ch009
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Abstract

Intelligent computation refers to intelligence artificially realised through computation. This chapter reviews six intelligent computation techniques. They are: knowledge-based systems, fuzzy logic, inductive learning, neural networks, genetic algorithms, and swarm intelligent techniques. All of these tools have found many practical applications. Examples of applications in manufacturing are given in the chapter.
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Knowledge-Based Systems

Knowledge-based systems, or expert systems, are computer programs embodying knowledge about a narrow domain for solving problems related to that domain. An expert system usually comprises two main elements, a knowledge base and an inference mechanism. The knowledge base contains domain knowledge which may be expressed as any combination of “IF-THEN” rules, factual statements (or assertions), frames, objects, procedures and cases. The inference mechanism is that part of an expert system which manipulates the stored knowledge to produce solutions to problems. Knowledge manipulation methods include the use of inheritance and constraints (in a frame-based or object-oriented expert system), the retrieval and adaptation of case examples (in a case-based expert system) and the application of inference rules such as modus ponens (If A Then B; A Therefore B) and modus tollens (If A Then B; NOT B Therefore NOT A) according to “forward chaining” or “backward chaining” control procedures and “depth-first” or “breadth-first” search strategies (in a rule-based expert system). With forward chaining or data-driven inferencing, the system tries to match available facts with the IF portion of the IF-THEN rules in the knowledge base. When matching rules are found, one of them is “fired,” ie. Its THEN part is made true, generating new facts and data which in turn causes other rules to “fire.” Reasoning stops when no more new rules can fire. In backward chaining or goal-driven inferencing, a goal to be proved is specified. If the goal cannot be immediately satisfied by existing facts in the knowledge base, the system will examine the IF-THEN rules for rules with the goal in their THEN portion. Next, the system will determine whether there are facts that can cause any of those rules to fire. If such facts are not available they are set up as subgoals. The process continues recursively until either all the required facts are found and the goal is proved or any one of the subgoals cannot be satisfied, in which case the original goal is disproved. Both control procedures are illustrated in Figure 1. Figure 1(a) shows how, given the assertion that a lathe is a machine tool and a set of rules concerning machine tools, a forward-chaining system will generate additional assertions such as “a lathe is power driven” and “a lathe has a tool holder.” Figure 1(b) details the backward-chaining sequence producing the answer to the query “does a lathe require a power source?.”

In the forward chaining example of Figure 1(a), both rules R2 and R3 simultaneously qualify for firing when inferencing starts as both their IF parts match the presented fact F1. Conflict resolution has to be performed by the expert system to decide which rule should fire. The conflict resolution method adopted in this example is “first come, first served”: R2 fires as it is the first qualifying rule encountered. Other conflict resolution methods include “priority.” specificity” and “recency.”

Figure 1.

(a) An example of forward chaining; (b) an example of backward chaining

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