Performance enhancement of electrochemical honing process using ANN approach for bevel gear finishing

摘要

This paper reports on performance enhancement of electrochemical honing (ECH) through its multi-performance optimisation for finishing the bevel gears. Implicit nature of ECH, complex interactions among its process parameters and conflicting objectives makes the multi-performance optimisation very challenging. In such cases, soft-computing tools such as artificial neural network (ANN) are found to be very effective. In this work, multi-performance optimisation of five important ECH parameters (namely concentration, temperature and flow rate of the electrolyte, rotary speed of the workpiece gear and voltage) was performed by developing back-propagation neural network (BPNN) and multiple regression models of percentage improvement in average and maximum surface roughness and MRR. The experiments were conducted using Taguchi's L_(27) (3~(13)) orthogonal array to generate the experimental data required for the models. The optimised results were validated through confirmation experiments. The BPNN-based models were found very effective and superior to regression models for the multi-performance optimisation. Use of optimal values of the ECH parameters yielded better surface finish and productivity of the gears.