Fuzzy MCDM Methodology for Analysis of Fibre Laser Cutting Process

In many production technologies efficient process planning implies a careful selection of process parameters with respect to different techno-economic criteria. In the application and adoption of technological procedures, apart from specific technological knowledge and experience, different methodologies are being used, including empirical modelling and optimization, Taguchi’s robust design, artificial intelligence, fuzzy logic, and multi-criteria decision-making (MCDM). Considering the complexity of laser cutting technology, and difficulties and limitations when applying traditional MCDM methods, this study proposes a fuzzy MCDM methodology for the analysis of the fibre laser cutting process, assessment of alternative cutting conditions and selection of favourable cutting conditions. The experiment in fibre laser cutting of mild steel was based on a Box-Behnken design by considering three input parameters (focus position, cutting speed and oxygen pressure) and four relevant criteria for the assessment of cutting conditions (kerf width on a straight and curved cut, surface roughness and surface productivity). The proposed fuzzy MCDM methodology makes use of expert knowledge and experimental data for criteria evaluation and decision matrix development, respectively, while three fuzzy MCDM methods (fuzzy TOPSIS, fuzzy WASPAS, and fuzzy ARAS) are used to determine the complete ranking of alternatives. Kendall’s tau-b and Spearman’s rho correlation tests were applied to compare the obtained ranking lists, while the stability of ranking was assessed with the application of the Monte Carlo simulation. Finally, to approximate the fuzzy decision-making rule, a second-order model was developed and analysed so as to gain insight into the significance of process parameters and to identify the process window with the most favourable laser cutting conditions.