Prediction of Laser Cut Quality for Tungsten Alloy Using the Neural Network Method

Abstract

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.