Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision-making problems. The authors attempted to create two novel hybrid MCDM systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex PRoportional ASsessment (COPRAS). To demonstrate the ability and effectiveness of these two hybrid models i.e., TOPSIS-ARAS and COPRAS-ARAS were applied to solve a real-time robot selection problem with 12 alternative robots and five selection criteria, while evaluating the parametric importance using the CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. The rankings of the robot alternatives gained from these two hybrid models were also compared to the obtained results from eight other solo MCDM tools. Although the rankings by the applied methods slightly differ from each other, the final outcomes from all of the adopted techniques are consistent enough to suggest that robot 12 is the best choice followed by robot 11, and robot 4 is the worst one among these 12 alternatives. Spearman Correlation Coefficient (SCC) also reveals that the proposed rankings derived from various methods have a strong ranking relationship with one another. Finally, sensitivity analysis was performed to investigate the effects of weight variation and to validate the robustness of the implemented MCDM approaches.

https://www.mdpi.com/2073-8994/13/8/1331/pdf

Analysis of a Robot Selection Problem Using Two Newly Developed Hybrid MCDM Models of TOPSIS-ARAS and COPRAS-ARAS

Wire-arc additive manufacturing (WAAM) has recently attracted researchers to produce metal components with a high-deposition rate. Many researchers are trying to establish the WAAM process as a high-deposition metal additive manufacturing (AM) process. A computationally efficient mathematical model used for the metal-AM process yields a framework for component qualification as per international standards. Since heat transaction in the AM process can control the thermal-field generated through continuous heat deposition. Therefore, the present study focuses on the critical review of multi-physics continuum modelling of WAAM at a macro-scale level. Thermo-mechanical model for WAAM has been discussed extensively, i.e., heat-source models, materials models, meshing strategy, and boundary conditions. Further, a review of the simulation results discussed as thermal fields, residual stress and distortion, and experimental validation to provide a critique of real-life experiments. Also, various residual stress/distortion mitigation techniques used in the WAAM have been compiled to provide a framework for future directions.

Measurement and Mitigation of Residual Stress in Wire-Arc Additive Manufacturing: A Review of Macro-Scale Continuum Modelling Approach

This paper presents a multi-criteria decision making (MCDM) for a turning process. An experimental process was performed according to the sequence of a matrix using the Taguchi method with nine experiments. The parameters including workpiece speed, feed rate, depth of cut, and nose radius were selected as the input variables. At each experiment, three cutting force components that were measured in the three directions X, Y, and Z, were Fx , Fy , and Fz , respectively. The value of Material Removal Rate (MRR ) was also calculated at each experiment. The main purpose of this study is determination of an experiment in total performed experiments simultaneously ensuring the minimum Fx , Fy , and Fz and the maximum MRR . The Entropy method was applied to determine the weights for parameters Fx , Fx , Fx , and MRR . Eight MCDM methods were applied for multi-criteria decision making, this has not been performed in any studies. The implementation steps of each method were also presented in this paper. Seven ones of these eight methods determined the best experiment in total nine performed experiments. A new multi-criteria decision-making method as well as orientation for the further works were also proposed in this study.

/pdf/a-combination-method-for-multi-criteria-decision-making-57khbpepkc.pdf

A combination method for multi-criteria decision making problem in turning process

In the current study, near net-shaped selective laser melting (SLM) technology was employed to build nickel-based superalloy Inconel 625 (IN625) parts with good quality. Taguchi method was employed to formulate a systematical study, analyze, and optimize the influencing factors, i.e., laser power (LP), scan speed (SS) and hatch distance (HD) on the resulting micro-hardness (MH) and surface roughness (SR) of the build samples. Scanning electron microscope (SEM) and X-ray diffraction analysis were carried out to characterize the powder morphology (spherical shaped particle possessing the size of 35 Â± 6 I¼m) and the surface of the build samples. Laser power was the most contributing factor on the analyzed parameters (MH and SR), followed by the scanning speed and hatch distance. Taguchi determined optimal condition (MH: LP = 270 W, SS = 800 mm/s, HD = 0.08 mm; SR: LP = 270 W, SS = 800 mm/s, HD = 0.08 mm) which resulted in higher microhardness of 416 HV and lower surface roughness of 2.82 I¼m. Higher MH was attributed to the minimal porosity, while the uniform smooth surface of the build samples resulted in low SR as evident from the SEM images and surface texture analysis. Super ranking concept (SRC) was used to optimize the MH and SR simultaneously, by determining a single optimal condition (LP = 300 W, SS = 600 mm/s, HD = 0.10 mm). The obtained optimal condition resulted in a MH of 382 HV, and a SR of 3.92 I¼m. The results of optimal conditions are validated subjected to SEM morphologies. Â© 2021 The Authors

Experimental investigation of selective laser melting parameters for higher surface quality and microhardness properties: Taguchi and super ranking concept approaches

Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool

Recently, Additive Manufacturing (AM) has been widely used in many applications. For a particular AM component, the choice of available AM processes is critical to the component’s quality, mechanical properties, and other important factors. In that context, this article presents an efficient decision support system for the selection of an appropriate AM process. A novel hybrid Multi-Criteria Decision Making (MCDM) technique has been proposed to select an appropriate AM process from available AM processes. The Best Worst Method (BWM) is used to determine optimal weights of criteria and the Proximity Indexed Value (PIV) method is employed to rank the available AM processes. For benchmarking the abilities of an AM process, a conceptual model of spur gear was fabricated by four available AM processes viz., Vat Photopolymerization (VatPP), Material Extrusion (ME), Powder Bed Fusion (PBF), and Material Jetting (MJ). Additionally, Dimensional accuracy (A), surface roughness (R), tensile strength (S), percentage elongation (%E), heat deflection temperature (HDT), process cost (PC) and build time (BT) has been considered as most significant criteria. Further, sensitivity analysis has been performed to validate the reliability of the results. The results suggested that the Material Jetting (MJ) process produces dimensionally accurate and quality parts among available alternatives AM processes. The ranking obtained using the PIV method is consistent and reliable.

Jaivir Singh

Papers

A decision support system for the selection of an additive manufacturing process using a new hybrid MCDM technique

An experimental study on mechanical properties and wear of A380/nB4C composites fabricated using two different liquid state processes