Application of teaching learning based optimization procedure for the development of SVM learned EDM process and its pseudo Pareto optimization

Two-stage soft computing ((SVM-TLBO)-(PLM-TLBO-pseudo PARETO)) based virtual system of manufacturing process - EDM is developed.Virtual data generator of EDM process learned by support vector machine (SVM) with internal parameters (C, ? and ?) tuned by teaching learning based optimization (TLBO) is reported.Modifications namely population based termination criteria, initialize population with high dispersion and way of choosing teacher in case of multiple best learners performing same score, over standard TLBO are suggested. Further, a comparison between performances of TLBO and PSO in model development is studied.A simple procedure for pseudo Pareto front development by modified TLBO is proposed.Inverse solution procedure for selection of optimum available machine parameter setting corresponding to specific output combination is elaborated. Manufacturing processes could be well characterized by both the quantitative and the qualitative measurements of their performances. In case of conflicting type performance measures, it is necessary to get possible optimum values of all performances simultaneously, like higher material removal rate (MRR) with lower average surface roughness (ASR) in electric discharge machining (EDM) process. EDM itself is a stochastic process and predictions of responses - MRR and ASR are still difficult. Advanced structural risk minimization based learning system - support vector machine (SVM) is, therefore, applied to capture the random variations in EDM responses in a robust way. Internal parameters of SVM - C, ? and ? are tuned by modified teaching learning based optimization (TLBO) procedure. Subsequently, using the developed SVM model as a virtual data generator of EDM process, responses are generated at the different points in the experimental space and power law models are fitted to the estimated data. Varying the weight factors, different weighted combinations of the inverse of MRR and the ASR are minimized by modified TLBO. Pseudo Pareto front passing through the optimum results, thus obtained, gives a guideline for selection of optimum achievable value of ASR for a specific demand of MRR. Further, inverse solution procedure is elaborated to find the near-optimum setting of process parameters in EDM machine to obtain the specific need based MRR-ASR combination.

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