The cut quality is of great importance during the laser cutting process. The quality of laser cut mainly depends on an appropriate selection of process parameters. In this paper, the effect of process parameters was analysed on the laser cut quality of an uncommon alloy, the tungsten alloy (W ≈ 92.5 % and the remainder Fe and Ni) sheet with thickness of 1 mm. This alloy has a wide application in different industrial areas, e.g. in medical applications, the automobile sectors, and the aircraft industry. This paper introduces a developed back-propagation artificial neural network (BP- ANN) model for the analysis and prediction of cut quality during the CO2 laser cutting process. In the presented study, three input process parameters were considered such as laser power, cutting speed and assist gas type, and two output parameters such as kerf width and average surface roughness. Amongst the 42 experimental results, 34 data sets were chosen for training the network, whilst the remaining 8 results were used as test data. The average prediction error was found to be 5.5 % for kerf width and 9.5 % for surface roughness. The results of the predicted kerf width and surface roughness by the BP-ANN model were compared with experimental data. Based on the results of the study, it was shown that the proposed artificial neural network model could be a useful tool for analysing and predicting surface roughness and kerf width during CO2 laser cutting processes.

Abstract This paper defines mathematical models of value changes for surface roughness ( Ra , μm) and heat affected zone width ( HAZ , mm) during high-alloyed steel 1.4828 laser cutting using oxygen as an assistance gas. For the definition of appropriate mathematical models, multiple linear regression analysis is used, with four independent variables that were varied at five levels. Following parameters are varied: cutting speed ( V ), assist gas pressure ( p ), focus position ( fs ) and stand-off ( N d ). In comparison between the model and the experimental results, it can be concluded that the effects of specified parameters on cut quality, productivity and thus the legitimacy of this technology for cutting high-alloyed steels are well described by the obtained mathematical models.

Abstract Laser beam machining (LBM) is one of the most widely used thermal energy based non-contact type advance machining process which can be applied for almost whole range of materials. This paper defines mathematical models for surface roughness prediction (Ra, μm) and width of heat affected zone (HAZ, mm) during laser cutting of alloy steels 1.4571 and 1.4828 with nitrogen as assist gas. For defining appropriate mathematical models multiple regression analysis is used with four independent variables. Following parameters are varied: cutting speed, focus position, nitrogen assist gas pressure and stand-off. Obtained mathematical models describe dependence of Ra and HAZ from varied process parameters.