A study on multi-criteria decision-making in powder mixed electric discharge machining cylindrical shaped parts

Abstract

In life as well as in engineering, many times, it is necessary to choose the best option among many different options. That will be more difficult when the criteria given for the selection contradict each other. For example, when external cylindrical grinding, the minimum surface roughness requirement necessitates a small depth of cut and feed rate. The material removal rate will be reduced in this case, and this requirement will conflict with the maximum material removal rate requirement. To solve the above problem, a very useful tool is multi-criteria decision-making (MCDM). In this paper, for the first time, MCDM results for powder mixed discharge machining (PMEDM) cylindrical parts of SKD11 tool steel with copper electrodes have been presented. In this work, eighteen experiments with the L18 (16×53) design using the Taguchi method were conducted. Six main input process parameters include the powder concentration, the pulse current, the servo voltage, the pulse on time, and the pulse off time. To select an alternative that simultaneously ensures two criteria including minimum surface roughness (RS) and maximum material removal speed (MRS), four different MCDM methods including MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), TOPSIS (Technique for order of preference by similarity to ideal solution), and EAMR (Area-based Method of Ranking) and two methods of criteria weight calculation including MEREC (Method based on the Removal Effects of Criteria) and Entropy methods were selected. The results of MCDM when PMEDM SKD11 tool steel cylindrical parts with two methods for weight determination and four methods for solving MCDM problem were evaluated. In addition, the best alternative to ensure simultaneous minimum RS and maximum MRS was proposed.

A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making

“…MEREC is most widely used in the following applications in engineering: selection of a phase change material [134], selecting truck mixer concrete pump [135], determining the parameters for external cylindrical grinding [136], determining dressing parameters for internal cylindrical grinding [137], dressing process for internal grinding [138], determining input parameters for CBN grinding [139], material selection of a lightweight aircraft wing spar [140], pallet truck selection [141], selecting optimal spray-painting robot [23], milling process [142], selection quality hole by ultrasonic machining process [143], prioritizing solid-state drivers [144], determining the best experiment for turning process [145], plasma arc cutting process optimization [146], selecting the schema of scissors mechanism [147], determining the process input parameters in wire-electrical discharge machining [148], powder-mixed electric discharge machining [149][150][151], cubic boron nitride grinding [152], and hole turning process [8]. Uses other than applications in these areas are as follows: food waste treatment technology selection [153], urban planning and development, specifically in the context of sustainable smart city development [154], urban transport planning and DM, with a focus on adapting transport plans to the COVID-19 pandemic [155], web and IoT-based hospital location determination [156].…”

“…For MEREC, it has been seen that the most popular of the weighting methods used in studies with MEREC are ENTROPY [133,134,136,138,142,144,145,[151][152][153] and CRITIC [34,132,133,144,146]. Other methods are SWARA II [122,124], I2TL-SWARA II (interval 2-tuple linguistic) [130], simplified BWM [131], Symmetry Point of Criterion (SPC) [133], SECA and CILOS [34], FUCOM [8], and AHP [126].…”

“…An examination of ranking methods used in conjunction with MEREC reveals the widespread utilization of TOPSIS and MARCOS methods. The studies in which these methods have been applied are as follows: TOPSIS [8,34,126,134,136,138,145,148,149,151,152,156], fermatean fuzzy TOPSIS [154], MARCOS [34,122,132,136,138,144,145,147,149,151], DNMAR-COS (double normalized MARCOS) [135], type-2 neutrosophic number MARCOS [127], single-valued neutrosophic sets MARCOS [140]. Additional employed ranking methods include MAIRCA [8,34,138,145,149,151,152], MABAC [8,23,34,136,137,143,150], CO-COSO [8,23,34,143], GREY-COCOSO [121], cumulative prospect theory COCOSO [130]...…”

Application of the multi-criteria analysis method mairca, spotis, comet for the optimisation of sustainable electricity technology development

“…That's why decision-making for sustainable electric energy policies are necessary. Many authors have explored this area and introduced various methods, such as the multi-criteria decision-making (MCDM) approach [3][4][5][6][7].…”

Comparison of MCDM methods effectiveness in the selection of plastic injection molding machines

“…The purpose of applying MCDM methods is to find the best option among available options. Therefore, the methods are considered to be equally effective when they find the same best option [2], [3], [4]. MCDM methods are divided into four main groups, the first group is the methods that need to normalize data and determine the weights for criteria (called group I), the second group is the methods that need to normalize data but do not need to determine the weights for criteria (group II), the third group is the methods that do not need to normalize data but need to determine the weights for criteria (group III), the fourth group is the methods that need neither data normalization nor determining the weights for criteria (group IV).…”