Identification of Patterns in Genetic-Algorithm-Based Solutions for Optimization of Process-Planning Problems Using a Data Mining Tool

This paper presents a novel use of data mining algorithms for extraction of knowledge from a set of process plans. The purpose of this paper is to apply data mining methodologies to explore the patterns in data generated by genetic-algorithm-generating process plans and to develop a rule set planner, which helps to make decisions in odd circumstances. Genetic algorithms are random-search algorithms based on the mechanics of genetics and natural selection. Because of genetic inheritance, the characteristics of the survivors after several generations should be similar. The solutions of a genetic algorithms for process planning consists of the operation sequence of a job, the machine on which each operation is performed, the tool used for performing each operation, and the tool approach direction. Among the optimal or near-optimal solutions, similar relationships may exist between the characteristics of the operation and sequential order. Data mining software known as See5 has been used to explore the relationship between the operation’s sequence and its attributes, and a set of rules has been developed. These rules can predict the positions of operations in the sequence of process planning

IMPROVING ENERGY EFFICIENCY IN DISCRETE PARTS MANUFACTURING

Ph.DDOCTOR OF PHILOSOPH

Modeling process planning problems in an optimization perspective

Proceedings - IEEE International Conference on Robotics and Automation31764-1769PIIA