Optimization of Surface Roughness in Plasma Arc Cutting of AISID2 Steel Using TLBO

Abstract This paper attempts the application of TLBO algorithm in order to analyze the effect of process parameters on surface roughness in plasma arc cutting of AISI D2 steel. Here, three process parameters cutting speed, gas pressure and torch height have been considered. Experiments have been conducted based on L16 orthogonal array. Here, average surface roughness have been measured for each experimental runs. The experimental data has been utilized in order to develop valid empirical models to relate aforesaid performance characteristic with machining parameters using non-linear regression analysis. The values of surface roughness predicted from empirical models are compared with experimental results and the percentage relative error within 3.7781% is observed. Finally, latest evolutionary approach known as Teaching learning based optimization (TLBO) algorithm has been proposed to obtain favorable machining conditions through optimization of each aforementioned performance characteristic. Optimal results are also compared with genetic algorithm (GA), it has been noticed that TLBO provides the better result as compare to genetic algorithm