Taking full advantage of what laser cutting technology offers in terms of achieving superb quality cuts at low cost and high production rates requires the optimization of laser cutting parameters. This implies the need to formulate and solve different laser cutting optimization problems. In this article, an optimization model for CO₂ laser cutting of mild steel is developed. The laser cutting optimization problem was explicitly formulated as a single-objective optimization problem with five non-linear constraints of the equality, inequality and range type. The goal was to determine the laser cutting parameter values so as to maximize the material removal rate while simultaneously considering practical process constraints related to dross formation, kerf width, perpendicularity deviation, surface roughness and severance energy. Two crossed experimental designs of different resolutions were performed in order to define six mathematical models, which were used in the formulation of the optimization problem. For the purpose of optimization, the exhaustive iterative search algorithm was applied, since it determines solutions whose optimality is guaranteed in the given discrete space of input variable values. The practical usability of the developed laser cutting optimization model and the effectiveness of the applied optimization approach were proved while solving a real case study aimed at the optimization of laser cutting parameters for cutting parts for the furnace industry.

Selection of the most suitable nonconventional machining process (NCMP) for a given machining application can be viewed as multi-criteria decision making (MCDM) problem with many conflicting and diverse criteria. To aid these selection processes, different MCDM methods have been proposed. This paper introduces the use of an almost unexplored MCDM method, i.e. operational competitiveness ratings analysis (OCRA) method for solving the NCMP selection problems. Applicability, suitability and computational procedure of OCRA method have been demonstrated while solving three case studies dealing with selection of the most suitable NCMP. In each case study the obtained rankings were compared with those derived by the past researchers using different MCDM methods. The results obtained using the OCRA method have good correlation with those derived by the past researchers which validate the usefulness of this method while solving complex NCMP selection problems.

Determination of the most suitable manufacturing process conditions for a given application is very complex task and requires consideration of a number of conflicting and diverse process performance characteristics (criteria). In this paper the application of a recent multi-criteria decision making (MCDM) method, i.e. weighted aggregated sum product assessment (WASPAS) for determination of manufacturing process conditions in laser cutting was discussed. Laser cutting experiment was conducted based on Taguchi’s L9 experimental design by varying the laser power, cutting speed, assist gas pressure and focus position at three levels. Based on obtained experimental results, a MCDM model consisting of nine alternatives and six criteria was defined. In order to determine the relative significance of considered criteria a pair-wise comparison matrix of the AHP method was used. Stability of the obtained ranking of alternatives was checked by varying values of coefficient of linear combination and by the application of operational competitiveness ratings analysis (OCRA) method. Keywords: Multi-criteria decision making, manufacturing, laser cutting, WASPAS, OCRADOI: http://dx.doi.org/10.5755/j01.ee.27.2.13428

Laser cutting parameters synergistically affect, although in different quantitative and qualitative manners, multiple process performances, such as the resulting cut quality characteristics, material removal rate, cutting time, and costs, and the determination of the most appropriate laser cutting conditions for a given application is of prime importance. Given the existence of multiple mutually opposite performances, assessment and laser cutting conditions and performance can be considered a multiple-criteria decision-making (MCDM) problem. In order to overcome the possible inconsistency of rankings determined by different MCDM methods while solving the same decision-making problem, the present study promotes a novel methodology for the assessment and selection of laser cutting conditions by developing a robust decision-making rule (RDMR) that combines different decision-making rules from six MCDM methods and Taguchi’s principles of robust design. In order to illustrate the application of the proposed methodology, CO₂ laser cutting in a stainless-steel experiment, based on the use of the Box–Behnken design, was conducted. On the basis of the experimental results, a comprehensive laser cutting MCDM model was developed with seven criteria related to cut quality (i.e., kerf geometry and cut surface), productivity, variable costs, and environmental aspects. It was observed that there was no laser cutting condition that could be considered as the best regime with respect to the different laser cutting process performances. Kendall’s and Spearman’s rank correlation coefficients indicated a certain level of disagreement among the resulting rankings of the laser cutting conditions produced by the considered MCDM methods, whereas the application of the proposed RDMR ensured the highest level of ranking consistency. Some possibilities for modeling of RDMR and its further use for the assessment of arbitrarily chosen laser cutting conditions and the use of the derived model to perform sensitivity analysis for determining the most influential laser cutting parameters are also discussed and addressed. It was observed that laser cutting parameters in different laser cutting conditions may have a variable effect on the resulting overall process performances. The comparison of the obtained results and the results determined by classical desirability-based multi-objective optimization revealed that there exists substantial agreement between the most preferable and least preferable laser cutting conditions, thus justifying the applied methodology.

This paper presents an investigation into the effect of the laser cutting parameters on the heat affected zone in CO2 laser cutting of AISI 304 stainless steel. The mathematical model for the heat affected zone was expressed as a function of the laser cutting parameters such as the laser power, cutting speed, assist gas pressure and focus position using the artificial neural network. To obtain experimental database for the artificial neural network training, laser cutting experiment was planned as per Taguchi?s L27 orthogonal array with three levels for each of the cutting parameter. Using the 27 experimental data sets, the artificial neural network was trained with gradient descent with momentum algorithm and the average absolute percentage error was 2.33%. The testing accuracy was then verified with 6 extra experimental data sets and the average predicting error was 6.46%. Statistically assessed as adequate, the artificial neural network model was then used to investigate the effect of the laser cutting parameters on the heat affected zone. To analyze the main and interaction effect of the laser cutting parameters on the heat affected zone, 2-D and 3-D plots were generated. The analysis revealed that the cutting speed had maximum influence on the heat affected zone followed by the laser power, focus position and assist gas pressure. Finally, using the Monte Carlo method the optimal laser cutting parameter values that minimize the heat affected zone were identified.