Multi-objective optimization and modeling of age hardening process using ANN, ANFIS and genetic algorithm: Results from aluminum alloy A356/cow horn particulate composite

This study reports on the modeling and multi objective optimization of age hardening process parameters using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The age hardening outputs (hardness and cost) were predicted using ANN and ANFIS. ANFIS with a correlation coefficient (R) value of 0.9985 predicted the obtained hardness values better than ANN which had R value of 0.9926. For the process cost predictions, ANFIS and ANN obtained the same values of R which was 1. Later values outside the experimental data points were predicted with ANN and ANFIS. When the temperature was kept constant and other input parameters were varied, the average relative error of the predicted values for ANFIS was 0.016% and for ANN 0.0037%. When the temperature was varied and other input parameters kept constant, the average relative error of the hardness values predictions for ANFIS was 73.69% and ANN was 0.2229%. With better performance of ANN outside the experimental points, it was used as fitness function for multi objective optimization of the age hardening process parameters using genetic algorithm (GA). The results show that ANN with coarse experimental data points for learning is more effective than ANFIS in predicting process outputs in the age hardening operation of A356 alloy/CHp particulate composite. The fine experimental data requirements by ANFIS make it more expensive in modeling and multi-objective optimization of age hardening operations of A356 alloy/CHp particulate composite.