Investigation on machining of GFRP through ANOVA, DFA, and CoCoSo method combined with Taguchi approach

Abstract

Polymer-based composites (GFRP) are increasingly used in the mechanical industry due to their cost-profitability, durability, and good mechanical properties. In the present study, a metal carbide tool was employed to manufacture polyamide (PA66-GF30%) specimen in a dry environment. The impact of variations in cutting process parameters on the responses studied is investigated using two experimental techniques. The first is the OFAT approach. For the second technique (multifactorial), the effects of input parameters on technological parameters (Ra, Fz, MRR, and Pc) are quantified aided by the ANOVA method using an experimental design based on Taguchi's L9 (3 x 3) orthogonal matrix. Based on the RSM methodology, the results were processed statistically to propose prediction models for the different outputs. In addition, a single-objective optimization study of Taguchi's and a multi-objective optimization of the operating conditions using the classification method based on CoCoSo as well as the DF Approach according to two desired objectives were carried out. The first objective of this study concerns the minimization of (Ra, Fz, and Pc), and the second one considers the minimization of (Ra, Fz, and Pc) at the same time as maximizing (MRR). Finally, the surface roughness criteria (Ra and Sa) of PA66-GF30% were evaluated to study the effect of the feed (f) on the machined surface topography. The findings are of capital importance in that they provide the required and correct information about the working conditions of composite polymers. According to OFAT results, feed (f) is the predominant factor influencing roughness (Ra). Temperature in the cutting zone rises with increasing cutting conditions (Vc, f and ap). Variations in cutting conditions affect the morphology of the chip produced. The roughness criteria (Ra and Sa) results clearly show that the topography of the PA66-GF30% surface changes as (f) changes. Also, optimization of all performance parameters simultaneously indicates that the DFA offers the best combination of parameters: Vc = 187.50 m x min-1, f = 0.096 mm x rev-1, and ap = 0.866 mm, leading to the minimization of (Ra) and maximization of (MRR), with respective values of 1.24 mu m and 15.614 cm3 x min-1. Furthermore, the CoCoSo method favors the minimization of (Fz and Pc) with values of 20.18 N and 69.285 W, respectively.