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Optimization with the Evolution Strategy by Example of Electrical-Discharge Drilling

2017; Springer Nature; Linguagem: Inglês

10.1007/978-3-319-67180-2_12

2194-5357

Jan Streckenbach, Ivan Santibáñez Koref, Ingo Rechenberg, Eckart Uhlmann,

Advanced Multi-Objective Optimization Algorithms

A key challenge in electrical discharge machining (EDM) is to find a suitable combination out of numerous process parameters. Any changes, concerning the electrode materials or geometries, require newly optimized technologies. These technologies are to be developed from a considerable number of experiments which must be carried out by an experienced operator. This paper presents a new method of finding the optimal parameters. It seems likely that the evolution strategy (ES), a stochastic, metaheuristic optimization method, offers the great advantage of finding solutions, even with little knowledge of system behaviour. The method involved a randomized and a derandomized ES, based on a non-elitistical (μ,λ)-evolution strategy with one parent and four children. The two ES were initialized from an unfavourable starting point (A) and from a favourable starting point (B), to investigate their effectiveness. We demonstrate that starting from the unfavourable starting point A the processing duration tero could be reduced by a maximum of 77% with a slightly smaller linear wear of the tool electrode ΔlE after 40 trials.