Research on optimization of electrical parameters of EDM based on a neural network algorithm

Abstract

An electrical Discharge Machine (EDM) is an effective spark machine that passes the sparks to get the desired shape by performing metal fabrication. In the EDM process, materials are removed between the two electrodes by transferring the workpiece’s high electric voltage and dielectric liquid. The voltage between the two electrodes is gradually increased to break down the dielectrics and remove the materials from the surface. EDM utilizes several parameters to regularize the material removal rate during this process. EDM changes its parameter for every experiment to minimize the Average Tool Wear Rate (ATWR). The existing techniques utilize a neural model to optimize the EDM parameter. However, the traditional approaches fail to perform the fine-tuning process that affects the Material Removal Rate (MRR) and ATWR. Therefore, meta-heuristics optimization techniques are incorporated with the neural model to enhance the EDM parameter optimization process. In this work, the EDM experimental data have been collected and processed by the learning process to create the training pattern. Then, the test data is investigated using the Backpropagation Neural Model (BPM) to propagate the neural parameters. The BPM model is integrated with the Butterfly Optimization Algorithm (BOA) to select the search space’s global parameters. This analysis clearly shows an 8.93% maximum prediction rate, 0.023 minimum prediction rate and 2.83% mean prediction rate while investigating the different testing patterns compared to other methods.