|dc.description.abstract||This study aims to investigate the effects of laser process parameters on the cutting qualities of thin non-oriented silicon steel; then, the cutting qualities are predicted and optimized by the application of the artificial intelligence (AI) models. Firstly, four experiments are presented in the research: experiment I and experiment II study with the slow cutting speed, experiment III and experiment IV research in higher cutting speed. The heat-affected zone (HAZ) is examined in both four experiments because it has the most influence on the magnetic property of silicon steel material, considered by laser cutting in air and solvent conditions with slow and high cutting speed. Besides, the qualities such as geometry, surface roughness, cutting time, dimensional precision are also investigated. Experiment I examines the influences of the three laser process parameters on two cutting qualities of the kerf waviness and HAZ, which are the laser power, cutting speed and pulse repletion rate. Experiment II shows that cutting in water and sodium chloride achieve a smaller HAZ than cutting in air condition. Experiment II also indicates a negative correlation between HAZ and magnetic flux density. Experiment III studies the influences of laser process parameters on multi-qualities, cutting time (TC), kerf width (KT), kerf taper angle (), HAZ, and surface roughness (Sa). Experiment IV evaluates the influences of environmental conditions (air, deionized water, alcohol, lubricant oil, sodium chloride, and nital solution 5%, 10%, 15%) on the recast layer, dross/debris at the cutting edge. Due to the good quality at the cutting edge in the short cutting time, the nital 10% condition is selected for investigating the effects of three process parameters (P, v, f) on the seven cutting qualities of a motor core’s lamination, HAZ, dimensional precision (inner diameter error ED1, outer diameter error ED2, tooth width error EL, inner diameter roundness C1, outer diameter roundness C2), and cutting time (TC).
Then, four AI models, which are an extreme learning machine (ELM), multiple regression analysis (MRA), artificial neural network (ANN), and random forest (RF), are applied to predict the cutting qualities. Results reveal that among the four models used, the most accurate predictions are found by the ELM model. Besides, the random forests method is also addressed to determine the relative importance of input parameters associated with the responses. All four experiments show that laser power is the most significant influence on HAZ.
Finally, the ELM model is selected to optimize the cutting qualities due to the highest predictive response accuracy among the four AI tested models. By implementing the predicted optimal processing parameters for experiment II, the resulting Extreme learning machine - genetic algorithm (ELM-GA) for the optimization and experimental confirmation, the obtained MFD and HAZ is 1.639 T and 30.40 mm. A multi-objective optimization model, the ELM - GA algorithm based preference selection index (PSI) method, is developed to optimize the seven output qualities of laminated core in the nital 10% condition after comparing the predicted accuracy of three models applied (ELM, MRA, ANN). The experimental validation is performed to evaluate the accuracy of optimal prediction with HAZ of 33.4 µm, ED1 of 14 µm, ED2 of 19 µm, C1 of 21 µm, C2 of 26 µm, EL of 13 µm, and TC of 34.14 s, which errors between optimal and experimental confirmation were 4.04%, 6.52%, 4.02%, 0.48%, 2.14%, 5.09%, and 1.25% for HAZ, ED1, ED2, C1, C2, EL, and TC, respectively. Consequently, the merit of direct formation is ready-for-assembly core laminations without the need of any post-processing renders the proposed laser cutting scheme an economical and effective approach for manufacturing ready-for-assembly core laminations from the thin silicon steel sheets.||en_US|