Effect of process parameters on cut quality of stainless steel of plasma arc cutting using hybrid approach

Abstract

An optimization concept of the various machining parameters for the plasma arc cutting procedures on AISI 316 stainless steel conducting a hybrid optimization method has been carried out. A new composition of response surface methodology and grey relational analysis coupled with principal component analysis has been proposed to evaluate and estimate the effect of machining parameters on the responses. The major responses selected for these analyses are kerf, chamfer, dross, surface roughness and material removal rate, and the corresponding machining parameters concentrated for this study are feed rate, current, voltage and torch height. Thirty experiments were conducted on AISI 316 stainless steel workpiece materials based on a facecentered central composite design. The experimental results obtained are applied in grey relational analysis, and the weights of the responses were evaluated by the principal component analysis and further evaluated using response surface method. The results show that the grey relational grade was significantly affected by the machining parameters directly as well as with some interactions. This method is straightforward with easy operability, and the results have also been established by running confirmation tests. The premise attributes beneficial knowledge for managing the machining parameters to enhance the preciseness of machined parts by plasma arc cutting.

MCDM Optimization of Parameters for Wire-EDM Machined Stainless Steel using Hybrid RSM-TOPSIS, Genetic Algorithm and Simulated Annealing

“…Positive ideal solution and negative ideal solution are articulated in order to evaluate parting distance which is furnished in Table VI. Lastly, overall performance index (OPI) has been calculated using Equation (10). Mean effect plot for overall performance index has been shown in Fig.…”

“…Equation (1) shows the second-order model which describes the conduct of the method: (1) Where, Y = consistent output, X i = response, X i X j are squares and collaboration terms in second-order model, respectively. β 0 , β i , β ij , β ii are indefinite regression coefficients, ϵ is error in model [10][11][12][13][14].…”

Experimental investigations on SS 304 alloy using plasma arc machining

“…In this experimentation, MRR is calculated using the formula with respect to kerf width, cutting length, cutting thickness, and machining time as shown in Eqs. (1,2). As the SS 304 alloy material is fixed on the worktable of plasma arc machine, the MRR is calculated by using a formula based on the cutting length, thickness of the workpiece and kerf width as shown in the following formulae:…”

“…Maity et al [1] investigated PAC process by studying a hybrid optimization method on AISI 316 steel, and they found that torch height along with a feed rate is largely contributing to producing an enhanced cutting quality. Subbarao et al [2] used DOE techniques to investigate the influence of PAC variables on Hardox-400 material, and they proposed that irregularity in cut surface can be reduced by decreasing the cutting speed and other cut quality characteristics depend on arc voltage.…”

Prediction model of surface roughness parameters of structural steel created by plasma arc cutting via full factor experiment

“…Analogous to the first and second experiments, the response Rz is specified in the last column of Table 6. The individual values are the arithmetic mean of the 15 measurements made on each sample.…”