A hybrid model using supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-EDM

In micro-electrical discharge machining (EDM), processing parameters greatly affect processing efficiency and stability. However, the complexity of micro-EDM makes it difficult to determine optimal parameters for good processing performance. The important output objectives are processing time (PT) and electrode wear (EW). Since these parameters influence the output objectives in quite an opposite way, it is not easy to find an optimized combination of these processing parameters which make both PT and EW minimum. To solve this problem, supporting vector machine is adopted to establish a micro-EDM process model based on the orthogonal test. A new multi-objective optimization genetic algorithm (GA) based on the idea of non-dominated sorting is proposed to optimize the processing parameters. Experimental results demonstrate that the proposed multi-objective GA method is precise and effective in obtaining Pareto-optimal solutions of parameter settings. The optimized parameter combinations can greatly reduce PT while making EW relatively small. Therefore, the proposed method is suitable for parameter optimization of micro-EDM and can also enhance the efficiency and stability of the process.