Abstract 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 qualities of silicon steels with a thickness of 100 μm. The three parameters considered were the laser power, cutting speed and pulse repetition rate and the two qualities 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 qualities 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.

中文翻译：

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一种用于预测薄无取向硅钢脉冲激光切割切口波纹度和热影响区的极限学习机

摘要 由于较低的磁芯损耗和较高的磁通密度和磁导率，薄无取向硅钢在软磁材料中变得越来越重要。最近，激光已成为加工薄硅钢的一种经济高效的工具，特别是用于小批量和多品种电机制造。基于实验数据，本研究旨在开发一种极限学习机（ELM），用于预测厚度为 100 μm 的硅钢的激光切割质量。考虑的三个参数是激光功率、切割速度和脉冲重复率，监测的两个质量是切口波纹度和热影响区 (HAZ)。每个参数被指定为四个级别，总共进行了64组实验参数。实验结果表明，两种切削质量都与这三个参数呈正相关。我们随机抽取了 80% 的实验数据用于模型训练，其余 20% 用于模型测试。为了验证 ELM 的适用性和优势，还基于相同的数据集开发了两个辅助模型人工神经网络和全二次多元回归分析 (MRA) 进行比较。结果表明，ELM 可以很好地预测波纹度和 HAZ，并提供了三个模型中最准确的预测。波纹度和 HAZ 的误差分别为 2.90% 和 4.16%。因此，开发的 ELM 对于波纹度和 HAZ 估计是实用且有效的。此外，基于随机森林方法，