Modeling and optimization in turning of PA66-GF30% and PA66 using multi-criteria decision-making (PSI, MABAC, and MAIRCA) methods: a comparative study

Abstract

Semi-crystalline polymers are widely used in modern industry. Indeed, they are highly coveted because of their excellent compromise between advantageous mechanical properties, high lightness, good productivity and low cost. In this work, a modeling study of performance parameters such as;(Ra), (Fz), (Pc) and (MRR) was carried out using the surface response methodology (RSM). The machining operations were performed dry on two polyamides (PA66-GF30% and PA66) following the L9 (3 3 ) orthogonal array. The results were used to perform a mono-objective optimization based on the Taguchi signal-to-noise ratio (S/N). In addition, a comparative study between three multiobjective optimization methods MCDM (PSI, MABAC and MAIRCA) coupled with the Taguchi approach was realized. The target objective is to reduce (Ra, Fz and Pc) and maximize (MRR) simultaneously. The results found are original and can help researchers working in the field of machining polyamides with and without reinforcement.

The Effect of Tool’s Rake Angles and Infeed in Turning Polyamide 66

“…The experimental results of this research showed that the most effective factor on cutting force is the cutting depth, in accordance with the findings in [17,19,20]. Authors in [5] obtained lowest cutting force at lowest simultaneous cuttingedge angles of 45° without noticing inconsistent drop in the forces at the end of the cutting passes, as was occurred in the current study.…”

“…The findings showed that when turning polyamide PA66 with the K15-KF carbide tool the highest force was recorded, followed by similar results for CVD and PCD, and the lowest force was recorded with the K15 carbide. Authors in [17] performed an experimental and analytical research to examine the surface quality, effective cutting force, and specific cutting pressure under the impact of three test variables of feed, depth of cut, and cutting speed during machining polyamide PA66. The obtained response data were optimized with ANOVA.…”

Selection of cutting insert for longitudinal turning of unalloyed steel using robust decision making

“…The first approach is usually based on the application of Taguchi's robust design methodology (Kumar et al, 2018;Dutta & Narala, 2021;Jadeja & Zala, 2021;Vijay HamaSur & Abdalrahman, 2023) and its hybridization with other methods, such as PCA, GRA, utility concept, etc., for handling multi-response optimization problems (Bagal et al, 2019;Viswanathan et al, 2020;Sristi et al, 2022;Dragičević et al, 2023). Likewise, a number of MCDM and fuzzy MCDM methods are being proposed for determining optimized turning parameters with respect to multiple criteria of different relative importance (Prakash & Krishnaiah, 2017;Thien et al, 2021;Chowdhury et al, 2023;Haoues et al, 2023). While the application of the Taguchi method can reveal certain near optimal turning parameter combinations, that were not previously included in the experimental matrix, the application of MCDM methods determines the most favourable (optimal) turning parameter combination from the previously known set of alternative parameter combinations.…”

Selection of straw waste reinforced sustainable polymer composite using a multi-criteria decision-making approach

“…MCDM techniques are prospective quantitative ways to solve decision issues with a finite number of alternatives and criteria (that are material properties) [18,19]. Over the past years, several researchers have investigated the problem of polymer composite selection optimization with different techniques, including AHP, ARAS, TOPSIS, MAIRCA, WASPAS, MABAC, MEW, MOORA, and VIKOR, to name a few [20][21][22][23][24][25][26][27]. The criteria weight affects the alternative rankings in most of these techniques that are hard to understand and difficult to apply since they need much mathematical expertise.…”

“…The exquisiteness of the PSI technique is that it is useful in evaluating optimal alternatives when there is disagreement about the relative relevance of the criteria, and it also requires less numerical calculations. The PSI technique was prosperously utilized in the ranking of polyamides by Haoues et al [25], in the selection of ship body materials by Gangwar et al [29], in the selection of materials for marine applications by Yadav et al [30], optimizing solar air heater parameters by Chauhan et al [31], ranking barriers to green human resource management implementation by Tweneboa Kodua et al [32] and in several other studies too [33,34].…”