Multi-Response Optimisation of Process Parameters in Pocket Milling Using Artificial Neural Networks and Genetic Algorithms

In the plastic industry for mold making, pocket milling is applied. The surface finish of the mold affects the quality of the plastic product, especially for toys. This can be achieved by minimising the surface roughness of the mold. To get a good quality product with a better production rate, the selection of the best combination of parameters in pocket milling is necessary. Multi-response optimisation can be applied for selecting such parameters which are suited for fulfilling the objective. In this study, one of the toy mold designs is selected as a pocket profile on which, two tool trajectories, viz Follow Periphery (FP) and Zigzag (ZZ), are applied for generation of pocket by varying Speed (S), Feed (F) and Step Over (SO). Box–Behnken Response Surface Methodology is applied to find the experimental runs. Two conflicting objectives minimising Surface Roughness (SR) and maximising Material Removal Rate (MRR) are obtained by applying Artificial Neural Networks (ANN) and Multi-Objective Genetic Algorithm (MOGA). Conformational experiments were conducted for the random set of Pareto results obtained from MOGA for both the tool trajectories to validate the model. From the analysis, it is observed that the FP tool path strategy is well suited to generate the pocket to get minimum SR and maximum MRR as the error percentage between the predicted and test results observed is 0.8085% for SR and 0.9236% for MRR.