Multiple benefits and advantages that offer laser cutting technology are difficult to achieve if the laser cutting parameter conditions are not adequately set. Determining laser cutting conditions is not trivial task considering large number of controllable inputs, existence of multiple process performances which are often mutually opposed as well as effects of noise factors. Different scientific methods and engineering approaches represent more sophisticated approaches which aid assessment and determination of the most favourable (optimized) cutting conditions. This study is focused assessment and ranking of laser cutting conditions in CO2 laser cutting of AISI 316L stainless steel. Based on kerf width and surface roughness experimental data and estimation of material removal rate (MRR) and total variable costs, four criteria in the multi-criteria decision making (MCDM) model were considered. For solving the developed model, six well-known MCDM methods were applied, and in addition, in order to overcome ranking inconsistency, robust decision-making rule was developed upon which final ranking was obtained.

The problem of cutting difficult-to-machine materials used in the aerospace industry, aircraft industry, and automobile industry, led to the development and application one of today?s most attractive technology for contour cutting - abrasive water jet cutting. For the efficient use of abrasive water jet cutting, it is of great importance to analyze the impact of process parameters on performance indicators, such as cutting quality, productivity, and costs. But also, from the energy utilization point of view, it is very important to analyze the impact of these parameters on the specific cutting energy which represents the amount of energy spent on the removal of material in the unit time. Having this in mind, this study presents the experimental results of abrasive water jet cutting of aluminum alloy with the aim of creating a mathematical model for estimating specific cutting energy as an important indicator of the degree of utilization of the available energy in the cutting process. The mathematical model of the specific cutting energy is explicitly represented as a non-linear function of the process parameters, obtained by the artificial neural network.

Upon different traditional and unconventional contour cutting technologies, fiber laser cutting technology is increasingly emerging as the most adequate choice for modern industrial production. In this paper the basics, specific characteristics and associated benefits and opportunities, as well as latest research and development directions in fiber laser cutting technology were discussed. In addition, the study presents results obtained by realization of two pilot experiments, based on the use of one factor at a time (OFAT) experimental strategy, in fiber laser cutting of mild steel. The first OFAT experiment considered the analysis and modelling of the effect of focus position on the kerf width and surface roughness, while the second OFAT experiment investigated the effect of the cutting speed. Based on the analysis of experimental results more adequate parameter settings than initial were identified which was one of the underlying motives and goals of the present study.

Considering the complexity of laser cutting technology, and difficulties and limitations when applying traditional multi-criteria decision-making (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) were 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 the 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 to reveal the significance of process parameters and identify favourable laser cutting conditions.

Geometrical characteristics of obtained kerf are important quality indicators in laser cutting. Considering that they are differently affected by a number of factors, this study is focused on the analysis of kerf width and its variation in CO2 laser...