40th International Conference on Production Engineering of Serbia
ICPES 2025
Nis, Serbia, 18-19th september 2025
AI-BASED PREDICTION OF KERF WIDTH AND SURFACE ROUGHNESS IN CO2 LASER CUTTING OF STAINLESS STEEL
A. Nagadeepan, C.S. Tamil Selvan, S. Viswanathan, B.Vishnu, V. Senthilkumar
DOI: 10.46793/ICPES25.030N
Light Amplification by Stimulated Emission of Radiation or LASER, is a thermal energy-based unconventional machining method. CO2 laser cutting of AISI 314 Stainless steel is carried out to evaluate the variation of kerf width in the cut section. Back-propagation Artificial Neural Networks are used to analyse and predict the kerf width during CO2 laser cutting. In this study, input parameters considered were cutting speed, power, stand off distance and gas pressure. For experimental database of artificial neural network L16 taguchi orthogonal array with four levels for each input parameter was proposed. Among the 16 datasets, 12 datasets were used for training the network and the remaining 4 datasets were used for testing the network. The results of predicted roughness and kerf width by back propagation neural network were compared with experimental data and the average predicting error on training datas was 0.37% and the average predicting error on testing datas was 4.34%, which confirms that the predicted ANN model might be utilised to study the impact of CO2 laser cutting settings on kerf width.
CO2 laser, Cutting speed, Power, Gas pressure, Stand off distance, ANN and Kerf width.
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