Monitoring and optimization of machining process when turning of AISI316L based on response surface methodology, artificial neural network and desirability function.

Abstract

The object of this research is to investigate the effect of cutting parameters such as cutting speed (Vc), feed rate (f) and depth of cut (ap) on machining parameters including cutting temperature (TC) and tool flank wear (VB) during dry turning of AISI 316L using coated carbide tool. The experiments were conducted according to Taguchi L27 orthogonal array, RSM and ANN have been used. Results revealed that (ap) found to be the dominant factor for TC. VB mainly influenced by Vc, f and ap, respectively. The prediction results obtained by ANN and RSM models showed a good agreement with experimental data. However, ANN models proved their capability to provide more accurate results compared to RSM models. According to the optimization analysis, Desirability function showed good accuracy in optimization.

Assessment of performance parameters in intermittent turning and multi-response optimization of machining conditions using DF, MOORA, VIKOR, and coupled NSGAII-VIKOR methods

“…It is also worth noting that in this case study, the weighting for the eight (08) outputs is the same (𝝎 𝒊 =1/8). The values of the normalized matrix are presented in Table (12). The solutions proposed by this approach are ranked in Table (13).…”

“…The Pareto- optimal solutions, along with the corresponding values of performance metrics, are reported in Table (18). Given that each parameter within the first group to minimize (Ra, Pm, VB, Ct, Fz, and Az) varies inversely with respect to MRR, a two-dimensional representation of the Pareto front for each minimizing output parameter in terms of (1/MRR) is depicted in Figure (12).…”