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, ɛ and σ) 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.