| dc.description.abstract | Non-oriented electrical steel sheet, emerging as an excellent core material for electrical machinery especially stators and rotors of electric motors due to its good magnetic properties. The need to improve electrical steel sheet cutting processes and accuracy while maintaining a flexible process and lower costs are unavoidable given the manufacturing industry′s explosive growth and demand for electric vehicles (EV). Due to its non-contact nature, flexibility, availability for small-batch production, and ability to be directly integrated with a variety of assembly lines, laser cutting has emerged as a promising method of processing electrical steel sheets. However, the product quality such as kerf width, kerf waviness, and heat-affected zone (HAZ) of cutting electrical steel sheets using laser cutting mainly depends on the optimal setup of laser power, pulse repetition rate, and cutting speed. Choosing the values for the optimal laser cutting parameters is very challenging since affected by the nature of the laser itself and the characteristics of the materials. As many characteristics of a workpiece can be represented in the vibration signals, which may provide a useful alternative judgment to predict kerf width when cutting with a pulsed laser. For these reasons, this research aimed to investigate the potential of employing extracted features from vibration signals combined with artificial intelligence (AI) based predictive models for kerf width prediction of pulsed laser cutting of non-oriented electrical steel sheets. The analysis consisted of four primary parts. Firstly, three different kinds of AI-based predictive models have been explored i.e. machine learning (ML), deep learning (DL), and ensemble learning (EL). Every predictive model has advantages and drawbacks, the main objective of exploring and comparing them is to manage and achieve the highest possible prediction accuracy. Secondly, two strategies from preprocessing the input features into the DL predictive models were considered i.e. raw time domain vibration signals and the extracted features from the wavelet transformation technic. Thirdly, the optimum base wavelet selection and strategies to select the optimal hyperparameters were explored in several notable ML models. Lastly, two laser cutting mechanisms such as the laser scanner and the X-Y table were investigated and compared to assess the kerf width quality obtained from the experimental and EL models. The results indicate that the choice of vibration-extracted features as the input to the AI-based predictive models can provide acceptable prediction accuracy for predicting the kerf width. The prediction accuracy for DL models by using raw time domain vibration signals and selected features from wavelet transformation as the input features yield 6.00% and 5.75% of mean average percentages error (MAPE), respectively. The prediction accuracy for ML models by using selected features from optimal base wavelet as the input features yields 1.69% of MAPE. Meanwhile, the prediction accuracy by using raw time domain vibration signals combined with EL models and considering two types of laser cutting movements yields 5.50% MAPE for the XY-table cutting mechanism and 6.98% MAPE for the laser scanner cutting mechanism. In general, this study lays the groundwork for future research into developing a real-time AI-based predictive model for kerf width prediction in pulsed laser cutting of non-oriented electrical steel sheets. | en_US |