Predicting and Enhancing the Multiple Output Qualities in Curved Laser Cutting of Thin Electrical Steel Sheets Using an Artificial Intelligence Approach

This study focused on the efficacy of employing a pulsed fiber laser in the curved cutting of thin, non-oriented electrical steel sheets. Experiments were conducted in paraffinic oil by adjusting the input process parameters, including laser power, pulse frequency, cutting speed, and curvature radius. The multiple output quality metrics included kerf width, inner and outer heat-affected zones, and re-welded portions. Analyses of the Random Forest Method and Response Surface Method indicated that laser pulse frequency was the most important variable affecting the cut quality, followed by laser power, curvature radius, and cutting speed. To improve cut quality, an innovative artificial intelligence (AI) approach incorporating a deep neural network (DNN) model and a modified equilibrium optimizer (M-EO) was proposed. Initially, the DNN model established correlations between input parameters and cut quality aspects, followed by M-EO pinpointing optimal cut qualities. Such an approach successfully identified an optimal set of laser process parameters, even beyond the specified process window from the initial experiments on curved cuts, resulting in significant enhancements confirmed by validation experiments. A comparative analysis showcased the developed models' superior performance over prior studies. Notably, while the models were initially developed based on the results from curved cuts, they proved adaptable and capable of yielding comparable outcomes for straight cuts as well.