RSM Modelling, and Multi-Object Optimization of Turning Parameters for Polyamide (PA66) Using PCA and PCA Coupled with TOPSIS

Abstract

In this study, turning operations on polyamide PA66 with cemented carbide insert, were organized according to the L27 Taguchi design whose objective is the analysis of the cutting parameters on the output parameters (Surface roughness and cutting force), as well as on the calculated parameter (material removal rate (Q)). The results revealed that surface roughness is highly impacted by the feed rate, which accounts for more than 68% of the variance, followed by the cutting speed and finally the depth of cut. With respect to cutting force, depth of cut and feed rate have emerged as the most important terms.A mathematical model is then created to predict the surface roughness and cutting force. Finally, the optimal cutting regime leading to good surface quality with less cutting force and maximum productivity was examinedusing two multi criteria optimization methods namely PCA and PCA coupled with TOPSIS. The total desirability function was used as a decision criterion for evaluating the two optimization methods. The results demonstrate the potential superiority of the PCA-TOPSIS method over the PCA method.

Comparative study of turning two engineering plastics (POM-C and PA-6) and optimisation using GA, SA, GRA and COPRAS with and without weighting (Entropie, critic, Swara, ROC)

“…The increase in (f) generates an increase in the width of the grooves traced by the nose of the tool on the machined surfaces of the two polymers tested. This is the direct cause of the deterioration in the quality of the surface obtained [4,5,30]. On the other hand, the increase in f and apse translates into an increase in the section of the chip; consequently, the volume of the chip increases, which contributes directly to the rise in temperature in the cutting zone.…”

Multi-objective optimization of inconel 718 turning parameters using PCA-MARCOS and MARCOS-Taguchi

“…Relational Coefficient The OQPI ranking selects the optimal solution. [20] PCA-based Utility theory WS Weighted Score, the same as MPI [21][22][23] PCA-Based TOPSIS OPI Overall Performance Index [24,25] PCA-Based MOORA Weights for calculating the Assessment of the overall assessment value based on the PCA eigenvectors' squares. [26] Neuro-fuzzy-PCA-The weights used for training the neuro-fuzzy model are the square of the PCA eigenvectors.…”