The MCDM problem is very important and often encountered in life and in engineering as it is used to determine the best solution among various possible alternatives. In this paper, the results of the MCDM problem in the dressing process for internal grinding are presented. To perform this work, an experiment with six input parameters, including the depth and the time of fine dressing, the depth and the time of coarse dressing, non-feeding dressing, and dressing feed rate, was conducted. The experiment was designed according to the Taguchi method with the use of L16 orthogonal arrays. In addition, TOPSIS, MARCOS, EAMR and MAIRCA methods were selected for the MCDM to obtain the minimum SR and the maximum MRR simultaneously. In addition, the weight determination for criteria was implemented by MEREC and entropy methods. From the results, the best solution to the multi-criteria problem for the dressing process in internal grinding has been proposed.

In order to address the Multi-Objective Optimization Problem (MOOP) in building a two-stage helical gearbox, this work presents a novel application of the Multi-Criterion Decision-Making (MCDM) method. The aim of the study is to determine the optimal primary design factors that will increase gearbox efficiency while decreasing gearbox volume. Three main design parameters were chosen for assessment in this work: the first stage’s gear ratio, and the first and second stages’ Coefficients of Wheel Face Width (CWFW). In addition, the MOOP is divided into two phases: phase 1 solves the single-objective optimization problem to reduce the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the Entropy approach was picked to compute the weight criteria, and the MARCOS method was chosen as an MCDM method to handle the multi-objective optimization issue. The following are important characteristics of the study: Firstly, the MCDM method (MARCOS technique) was successfully applied to solve a MOOP for the first time. Secondly, this work has looked into power losses during idle motion to calculate the efficiency of a two-stage helical gearbox. The results of the study were used in the design of a two-stage helical gearbox in order to identify the optimal values for three important design parameters.

The findings of a study on the application of the MCDM technique to select the best input parameters in wire-cut electrical discharge machining (wire-cut EDM) 90CrSi tool steel are presented in this paper. The TOPSIS method was used in the study to solve the MCDM problem, and the Entropy method was used to compute the weights of the criteria. In this work, six input parameters including the cutting voltage VM, the pulse on time ton, the pulse off time toff, the servo voltage SV, the wire feed WF, the feed speed SPD, and the workpiece cutting radius R were investigated. Also, a 27-2 design experiment was performed and a total of 32 experimental runs were conducted. The MCDM problem was solved. According to the findings of this study, the best experimental setup is experiment No. 7 with the following input parameters: VM=9 (V), Ton=12 (s), Toff=13 (s), SV=25 (V), WF=8 (mm/min), SPD=4.5 (mm/min), and R=9 (mm).

This study applies Multi-Criteria Decision-Making (MCDM) methods to identify the optimal dressing parameters for the surface grinding of Hardox 500 steel. The investigation focuses on three key objectives: Surface Roughness (SR), Material Removal Rate (MRR), and Wheel lifespan (Lw). Five dressing variables were considered: non-feeding dressing (nn), fine dressing depth (df), fine dressing times (nf), rough dressing depth (dr), and rough dressing times (nr). Three MCDM methods—Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Simple Additive Weighting (SAW), and Evaluation based on Distance from Average Solution (EDAS)—were employed to solve the MCDM problem. Additionally, the Entropy technique was used to determine the criterion weights. A total of 16 experimental runs were conducted based on the L16 (44 x 21) design configuration. The analysis identified Option 7 as the optimal dressing mode, characterized by the input parameters: dr = 0.02 mm, nr = 3 times, df = 0.05 mm, nf = 3 times, and nn = 0. To validate the consistency of rankings obtained from the three MCDM methods, the Spearman’s rank correlation coefficient (R) was employed. The results demonstrated a strong correlation among the rankings, confirming the reliability of the proposed approach. These findings provide a robust framework for optimizing surface grinding parameters to enhance performance and productivity.