Multi-Objective Optimization of Input Machining Parameters to Machined AISI D2 Tool Steel Material

Poor surface finish on die and mould transfers the bad quality to processed parts. High roughness is an example of that normally reduced by manual polishing after conventional milling machining process. Therefore, in order avoid disadvantages disadvantage machining, a sequence two operations proposed. The main operation run followed Rotary Ultrasonic Machining Assisted Milling (RUMAM). However, this requires optimum input parameters generate lowest roughness. Hence, paper aims optimize for both three soft-computing approaches – Genetic Algorithm, Tabu Search, Particle Swarm Optimization. method adopted begins with fitness function development, optimization approach usage ends up result evaluation validation. outperforms experiment having minimum Based findings, conclusion suggests lower can be obtained applying at maximum cutting speed vibration frequency, feed rate. This finding assists manufacturers apply proper values obtain parts