The Use Of Tabu Search And Particle Swarm Algorithm In Optimizing Edm Input Parameters To Machined Aisi L3 Tool Steel

Abstrak

Tool steel material is dominating as cutting tool material because of its good qualities and economical in production line. Besides, EDM Die Sinking is one of the common non-conventional machining in the industry that machine tool steel material. In order to obtain good machined tool steel work piece, and align in this case, the influence input parameters to machine by EDM Die Sinking must be studied. This context is ideal for the study of optimization, which optimized input parameters can be obtained to result good output parameters. Therefore, this paper aims to optimized EDM Die Sinking input parameters by using two soft-computing approached namely Tabu Search and Particle Swarm Optimization. The selected input parameters are Current, Pulse on time, and Pulse off time; and the outputs are Material Removal Rate, Electrode Wear Rate, and Surface Roughness. Result from this study finds that the proposed approaches are successfully obtained better results than through 3D Response Surface approach used in previous study. In addition, the predicted optimized input parameters are more economical to be applied in production line. Overall, this study helps manufacturers reduce production expenses for having a good output at lower rate of current supply.