Turning performance analysis and optimization of processing parameters using GRA-PSO approach in sustainable manufacturing

Abstract

The main aim of this research work is to examine the impact of input process factors (cutting speed, feed rate, and depth of cut) on machining characteristics (arithmetic mean surface roughness, tool flank face wear and tool chip interface temperature) and further to optimize during turning of Incoloy alloy 800. The cutting tool used was PVD multilayer coated (TiAlN-TIN) carbide insert. The experiment was performed based on Taguchi L27 methodology with a focus on the sustainability of turning. Sustainable manufacturing is considered for accomplishing overall efficiency with regard to economic, environmental and social aspects. The ANOVA (Analysis of Variance) was employed to assess the contributions of input process variables on cutting variable outputs. Further, the main effect diagrams exemplified the influences of main parameters on response variables. ANOVA analysis results demonstrated that the feed rate was the most leading factor that affects average surface roughness. The relationship(s) among input process factors and the evaluated measured outputs are determined using a quadratic regression model. Taguchi grey relational analysis has been implemented to trial results so as to optimize responses. The results revealed that the grey relational grade is significantly improved (0.308) through the setting of optimal parametric combinations. This integrated Grey–Taguchi approach was established quite effectual for simultaneously optimization of multifaceted machining output responses of turning process. This study provides a novel strategy to develop the machining efficiency of Incoloy 800 steel toward the improvement of sustainable processes and provide suitable applications in aerospace industry.

Statistical analysis, modeling and multi-objective optimization of parameters intermittent turning process of AISI D3

“…Also, the desirability function approach is among the multi-objective optimization methods extensively used in many industrial sectors because of the advantages it presents [21][22][23][24][25][26][27][28]. Several researchers have adopted the GRA method as a multi-objective optimization method for cutting conditions due to its effectiveness in making the right decision for selecting the optimal parameters [29][30][31][32][33][34][35].…”