Evolutionary Optimization of Machining Processes |
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Authors: | JingYing Zhang Steven Y Liang Jun Yao Jia Ming Chen Jing Li Huang |
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Affiliation: | (1) Advanced Manufacturing Engineering, Delphi Technical Center, Brighton, MI 48114, USA;(2) George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0504, USA;(3) Shanghai Machine Tool Works, Shanghai, 200093, P.R. China |
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Abstract: | Optimization of machining processes plays a key role in meeting the demands for high precision and productivity. The primary
challenge for machining process optimization often stems from the fact that the procedure is typically highly constrained
and highly non-linear, involving mixed-integer-discrete-continuous design variables. Additionally, machining process models
are likely discontinuous, non-explicit, or not analytically differentiable with the design variables. Traditional non-linear
optimization techniques are mostly gradient-based, posing many limitations upon application to today’s complex machining models.
Genetic Algorithms (GAs) has distinguished itself as a method with the potential for solving highly non-linear, ill-behaved
complex machining optimization problems. Unlike traditional optimization techniques, GAs start with a population of different
designs and use direct search methods stochastically and deterministically toward optimal and feasible direction. However,
GAs still has its own drawbacks when it is applied to machining process optimization, including the lack of efficiency due
to its binary representation scheme for continuous design variables, a lack of local fine-tuning capabilities, a lack of a
self-adaptation mechanism, and a lack of an effective constraint handling method. A novel and systematic evolutionary algorithm
based on GAs is presented in this paper in the areas of problem representation; selection scheme; genetic operators for integer,
discrete, and continuous variables; constraint handling method; and population initialization to overcome the underlying drawbacks.
The proposed scheme has been applied to two machining problems to demonstrate its superior performance. |
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Keywords: | Genetic algorithm Binary implementation Floating point implementation (α μ ) – Population initialization Constraint Handling Adaptive mutation and cross-over Non-uniform mutation Uniform crossover |
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