Experimental Investigation and Optimization of Cutting Parameters in Plasma Arc Cutting

Abstract

Experimental investigation of plasma arc cutting has been carried out using AISI 4140 and AISI 304 stainless steel as work-piece. The process parameters were considered as follows: feed rate, cutting current, cutting speed, gas pressure, voltage and torch height. The response parameters were chosen as follows: material removal rate (MRR), surface roughness (SR), right bevel angle (RBA), chamfer, dross, kerf width and heat affected zone (HAZ) which are the main cut quality characteristics of plasma arc cutting operation. The optimization of the process parameters have been carried out using desirability function, grey based principal component analysis (PCA) hybrid approach, genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA) and teaching-learning-based-optimization (TLBO) algorithm coupled with response surface methodology (RSM). A regression model was developed that represents the relationship between independent and dependent variables based on RSM. This type of novel approach has been proposed to evaluate and estimate the influence of plasma arc machining parameters on the quality of cut. This user-friendly mathematical approach is straight forward and the results thus obtained have also been validated by running confirmatory tests. The premise attributes provide beneficial knowledge for managing the machining parameters to enhance the preciseness of machined parts by plasma arc cutting. The obtained results indicate that the TLBO approach was significantly affected by the machining parameters directly with easy operability and economically.