Grey-fuzzy logic to optimise process parameters in drilling of glass fibre reinforced stainless steel mesh polymer composite

Abstract

Purpose The purpose of this paper is to optimise the process parameters, namely, point angle, spindle speed and feed rate in the drilling of glass-reinforced stainless steel mesh polymer (GRSSMP) composites using grey relational fuzzy logic. Design/methodology/approach Based on the full factorial design, the experiments were conducted. The output responses considered are thrust force, torque, delamination and diameter deviation. Based on responses, the optimised process parameter was selected using grey-fuzzy reasoning analysis (GFRA). Findings The percentage contribution of the drilling parameters is analysed using analysis of variance (ANOVA), and the result shows that feed rate is the most influential factor in the drilling of GRSSMP composites. Research limitations/implications The optimised drilling parameters have been used for drilling of polymer composites in the production industry. Originality/value Optimisation of process parameters during the drilling of GRSSMP composites using GFRA has not been performed previously.

Effect of drilling process parameters on bearing strength of glass fiber/aluminum mesh reinforced epoxy composites

“…Therefore, if the experimental run is time-consuming and expensive, it is advised to employ the Taguchi method 33 . Drilling parameters such as spindle speed, feed rate and drill point angle were selected in accordance with earlier literatures 18 , 20 , 24 , 25 , the constraints of the CNC machine, and early test runs where some of the drilling process' variables are determined by experimentation. Table 3 shows the experiments’ factors and their levels.…”

“…According to research by 19 , the delamination is directly connected to feed rate. The results demonstrated by Sakthivel et al 20 stated that feed rate is the most significant variable in drilling of Glass fiber Reinforced Stainless Steel Mesh Polymer composites. These findings agreed with research by Jenarthanan et al 21 .…”

Optimization of process parameters on drilling of natural fibres reinforced in epoxy resin matrices using Taguchi–Grey relational analysis

“…where, γ m is the Grey Relation Grade, m is the number of trials in the orthogonal line-up and γ j is the grey relational grade for the j th trial (Sakthivel et al, 2017). The total aggregate of the squared deviations SS T is broken down into the aggregate of the squared deviations SS d due to each drilling parameter and the aggregate of the squared error SS e .…”

“…where γ m is the average of the Grey Relational Grade, γ j is the average of the Grey Relational Grade at the optimized setting as discussed and q is the number of drilling parameters that particularly influences the multiple performance characteristics (Sakthivel et al, 2017). The process parameters which were obtained by trial, produce higher Grey Relational Grade values, are presented in Table 6.…”

Multi‐objective optimization of wear performance of epoxy composites by gray‐based response surface methodology

“…[15] Researchers have adopted various gray-based algorithms for optimization. [16,17] It can be viewed that the researchers have adopted gray analysis for various optimization problems. The inclusion of fibers augments the physical characteristics and improves wear performance of the polymer-based composites.…”