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Micro-EDM optimization through particle swarm algorithm and artificial neural network
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DOI：10.1016/j.precisioneng.2021.08.018.png)
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In the present study, an artificial neural network (ANN) together with a heuristic algorithm, called particle swarm optimization (PSO), was used to set up a methodology for selecting the optimal process parameters for the mu EDM process. The developed methodology is characterized by a double direction functionality responding to different industry needs. Usually, in the industrial scenario, the operators are bound by the project specifications or by the limited availability of time. For this reason, a methodology tested only on a specific workpiece material, that involves limited input parameters or developed for the optimization of a single performance is limiting. The developed 2-steps model leaves operators free to establish which factors to impose for the optimization and allows to define the best solution for the production of a part. The validation of the model shows a good fit between predicted and experimental results.
Keyword:
Artificial neural network
Particle swarm optimization algorithm
Micro-EDM
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