1. Introduction
Multi-criteria decision making (MCDM) is a common problem in practice when it is necessary to analyze different options to come up with the best alternative. This problem is posed not only for engineering but also for medicine, business, social sciences, and everyday life. In particular, it has been widely applied in mechanical processing because the machining process is often required to meet many criteria, such as the minimum machined surface roughness (SR), maximum material-removal rate (MMR), minimum cutting force, maximum tool life, or minimum machining cost. In fact, the criteria of a machining process often contradict each other. The requirement to increase the MMR will involve an increase in the depth of the cut and the feed rate, and it will lead to a growth in the surface roughness and a decrease in tool life. In addition, the requirement for a minor surface roughness will lead to a reduction in the depth of the cut and feed rate, and in turn, it will reduce the MMR. Therefore, solving MCDM problems through different methods has attracted many researchers.
The TOPSIS method is one of the most widely used MCDM methods [
1]. Besides its simplicity and practicality [
2], it can be applied to problems covering a lot of criteria and alternatives [
1]. This method was first proposed by Hwang et al. [
3], and it has been used in many mechanical machining processes, such as grinding [
4,
5], turning [
6,
7,
8], electrical discharge machining [
9,
10,
11], waterjet machining [
12,
13], etc. For the PMEDM process, it has been used to implement MCDM when machining Al
2O
3 ceramics [
14], Inconel 718 [
15], Inconel X-750 [
16], etc.
The MAIRCA (Multi-Attributive Ideal–Real Comparative Analysis) method was proposed in 2014 [
17] for the selection of railway crossings for investment in safety equipment. This method has the advantage that the objective function can be both qualitative and quantitative [
6]. In [
18], the MAIRCA method was used to rank and select the appropriate location for the construction of ammunition depots. Recently, it has been applied to MCDM in the turning process [
6].
The MARCOS approach was recently proposed by Stević, Ž. et al. [
19] when choosing sustainable suppliers in the healthcare industry in Bosnia and Herzegovina. This method is used for supplier selection in steel production [
20]. It has been used for MCDM for three methods of processing, including milling, grinding, and turning [
21]. In [
22], this method was used to select suitable gear material and cutting fluid.
A PMEDM process is understood as an EDM process with a dielectric solution mixed with metal powder in order to limit some disadvantages of the EDM process, such as low machined surface quality and small MMR. Like EDM, this type of machining is very effective when processing difficult-to-machine conductive materials and concave parts such as stamping dies and plastic molds. Therefore, there have been many studies on optimization or MCDM of PMEDM processes. The results of MCDM when PMEDM using titanium powder when machining SKD11 tool steel using the Preferred Selection Index (PSI) method have been shown in [
23], in which the minimum SR and maximum MRR are selected as criteria. J. Jayaraj et al. [
15] presented the selection of the best option when machining Inconel 718 using PMEDM with titanium powder. In this study, two criteria were SR and MRR, and the MCDM method was the TOPSIS method. The TOPSIS method was also applied in [
24] when solving the MCDM problem in PMEDM with mixed Si powder when processing EN-31 tool steel.
From the above analysis, it is obvious that there have been quite a few studies on MCDM for mechanical machining processes, including PMEDM, so far. Nevertheless, all of the studies on PMEDM have been carried out when machining concave parts or holes. Up to now, there has been no research on MCDM when machining cylindrically shaped parts, which are commonly used in shaped punches for stamping steel plates or tablet-shaped punches.
This paper introduces the results of an MCDM study when using PMEDM cylindrically shaped parts. In the study, minimum RS and maximum MRS were selected as the criteria for the investigation since RS and MRS are the two most important output parameters and the most popular subjects for the optimization study of mechanical machining processes [
25]. Additionally, three methods, including MARCOS, TOPSIS, and MAIRCA, were used for MCDM, and the MEREC method was used to determine the weights for the criteria. The evaluation of the results when solving the MCDM problem with different methods was performed. In addition, the best alternative to obtain minimum RS and maximum MRS simultaneously was suggested.
6. Conclusions
This article shows the results of a multi-criteria decision-making study when using PMEDM cylindrically shaped parts. In this work, 90CrSi tool steel was chosen as the workpiece material, and 100 nm SiC powder was mixed into the Diel MS 7000 dielectric. Moreover, five process factors, including the powder concentration, the pulse-on-time, the pulse-off-time, the pulse current, and the server voltage, were investigated. Additionally, the Taguchi method with the L18 (21 + 34) design was used to design the experiment, and three methods, including MARCOS, TOPSIS, and MAIRCA, were applied for multi-criteria decision making. Moreover, the determination of the weights for the criteria was performed using the MEREC method. The following conclusions were drawn from the research results:
This is the first time that MARCOS, TOPSIS, and MAIRCA methods have been used for the MCDM of a PMEDM process when processing cylindrically shaped parts.
Using all three above-mentioned methods identified the same best alternative.
The MAIRCA and the TOPSIS methods give quite similar ratings, proving that these two methods can be used interchangeably for MCDM when using PMEDM.
It was noted that the optimum set of the input factors for obtaining the minimum Ra and the maximum MRS simultaneously when processing cylindrically shaped parts was Cp = 0.5 (g/l), Ton = 8 (µs), Toff = 12 (µs), IP = 15 (A), and SV = 5 (V).
To further strengthen the reliability of the conclusions of this study, it is necessary to conduct multi-criteria decision-making studies with different weighting methods.