A laser is often considered to scribe the grain-oriented silicon steel surfaces after cold-rolling and annealing to reduce the core loss. It is necessary to select the best scribing parameters to maximize the reduction in this process. This paper proposes an optimization method of genetic algorithm during laser scribing of 30Q130 steel, by developing an artificial neural network prediction model using a database form a designed orthogonal experiment. The objective is to determine the best combination values of three important scribing parameters, namely scribing velocity, pulse energy and scanning spacing, that can get the largest core loss reduction. An optimized combination of parameters is obtained by this method and then validated by an adding experiment. The result indicates that the optimization model is reliable.%为了优化激光刻痕降低取向硅钢铁损的工艺,寻找刻痕速度、脉冲能量、扫描间距等重要刻痕参数的最佳匹配关系,提出了一种基于人工神经网络与遗传算法的优化模型,并利用这种模型对30Q130取向硅钢材料的刻痕工艺进行了优化实验,结果表明,这种模型稳定可靠,可以作为取向硅钢刻痕工艺优化的一种有效的措施.
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