Abstract In this study, a function approximation procedure is used, which called artificial neural network (ANN), according to the experimental results of the temperature of cutting region and surface roughness in cutting by the laser of AL6061T6 alloy. The cutting speed, laser power, sheet thickness, and assistant gas pressure as the inputs parameters and the surface roughness and cutting temperature as the target attributes are considered. The novelty of this study is shown by preparing 30 unalike ANN procedures to propose suitable architectures and training algorithms for them. The results of ANN are compared with the experimental results, and the error percent between them is derived. According to the comparison, the error percent between the experimental data and ANN is in a reasonable range, and this numerical method can be applied with low times and costs. For the cutting temperature, the mean value of the error percentages between the numerical results and experimental data is 0.66%. Also, for the surface roughness, the mean value of the error percentages is 5.79%.

中文翻译：

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AL6061T6合金激光切割人工神经网络模型预测与切割区温度和表面粗糙度实验结果对比

摘要 在本研究中，根据激光切割AL6061T6合金的切割区域温度和表面粗糙度的实验结果，采用了一种称为人工神经网络（ANN）的函数逼近程序。以切割速度、激光功率、板材厚度和辅助气体压力为输入参数，以表面粗糙度和切割温度为目标属性。通过准备 30 个不同的 ANN 程序来为它们提出合适的架构和训练算法，显示了这项研究的新颖性。将人工神经网络的结果与实验结果进行比较，得出两者之间的误差百分比。根据对比，实验数据与人工神经网络的误差百分比在合理范围内，并且这种数值方法可以以较低的时间和成本应用。对于切削温度，数值结果与实验数据的误差百分比平均值为 0.66%。此外，对于表面粗糙度，误差百分比的平均值为 5.79%。