Optimization of IGPH process parameters for the best gear precision using Box-Behnken and AICSA

摘要

Optimization of internal gearing power honing (IGPH) process parameters for the best gear precision has been carried out using Box-Behnken DOE method and artificial immune clone selection algorithm (AICSA).The analysis has been carried out in three stages.In the first stage, the second order models of tooth profile deviations have been developed in terms of four IGPH process parameters using response surface methodology based on Box-Behnken design.In the second stage,in order to solve the multi-objective optimization problem, a synthetic tooth profile deviation model has been built considering the different weighting factors of different deviations.In the third stage,the synthetic tooth profile deviation model was used in AICSA to determine the optimum input honing parameters for achieving the minimum synthetic tooth profile deviation.Both the Box-Behnken DOE method and AICSA-based models suggested a series of optimum process parameters, and the identified solutions were validated by experiments.AICSA-based model has been found to be a more accurate method for determining the optimum IGPH process parameters.