ANALYSIS OF MECHANICAL PROPERTIES OF CuZn35 WITH HEAT TREATMENT USING MACHINE LEARNING & TAGUCHI OPTIMIZATION

Abstract

Yellow brass is an alternative material for cartridge application. Heat treatment conducts on yellow brass to improve mechanical properties to fulfill the cartridge requirement. The main objective of this paper is to analyze the optimal parameters on the effect of heating temperature, holding time, and cooling medium on the tensile strength and hardness of yellow brass material as an alternative to CuZn30 replacement bullet casing material. Taguchi optimization and machine learning were carried out to evaluate several factors with a minimum number of tests using an orthogonal array experimental layout. The process parameters that will be optimized are furnace cooling media, water, and air. Annealing temperatures are 300, 400, and 500oC, and holding times are 60, 90, and 120 minutes. The results of the Taguchi method show that the parameter that has the most influence on the value of hardness and tensile strength is the heating temperature with a percent contribution of 85.8278% to hardness and 99.115% to tensile strength. On the machine learning results, the XG Boost model validation shows the MAE, RMSE, and R square values respectively 6.12; 8.23; 0.43 for the hardness response variable. The tensile strength response variable shows a value of 4.81; 7.76; and 0.49. Metrics from the validation show that a small sample using the Taguchi design can produce a good enough model to predict response.