An extreme learning machine for predicting kerf waviness and heat affected zone in pulsed laser cutting of thin non-oriented silicon steel

Due to lower core loss and higher flux density and permeability, thin non-oriented silicon steels are becoming more and more important for soft magnetic materials. Recently, laser has been emerged as a cost-effective tool for machining thin silicon steels, especially for the low-volume and high-variety motor manufacturing. Based on experimental data, this study aims at developing an extreme learning machine (ELM) for predicting the laser cutting qualifies of silicon steels with a thickness of 100 mu m. The three parameters considered were the laser power, cutting speed and pulse repetition rate and the two qualifies monitored were the kerf waviness and heat affected zone (HAZ). Each parameter was designated at four levels and totally 64 sets of experimental parameter were performed. Experimental results showed that both cutting qualifies were positively correlated with these three parameters. We randomly took 80% of the experimental data for model training while the remaining 20% was for model testing. To verify the ELM's appropriateness and advantage, two auxiliary models, artificial neural network and full quadratic multiple regression analysis (MRA), were also developed based on the same dataset for comparison. Results revealed that ELM well predicted waviness and HAZ and provided the most accurate predictions among the three models. The errors for waviness and HAZ were 2.90% and 4.16%, respectively. Consequently, the developed ELM was practical and effective for the waviness and HAZ estimations. Moreover, based on the random forests method, the relative significance of inputs associated with the responses was also addressed.