Laser selective metallization of ceramics (LMC) has been widely applied to prepare high-quality electrical patterns on ceramic circuit carriers. As the dominating factor, surface roughness after laser treatment is controlled mostly by the laser technical parameters, such as laser power, scanning velocity, and laser frequency. Therefore, establishing an accurate relation between the surface roughness and the laser parameters will be of great benefit to the LMC specimens. In the present research, machine learning is used to simulate the LMC process on an alumina-copper oxide ceramic. The effect of each laser parameter and the interaction between them are revealed by the multiple linear regression method. The artificial neural network model trained with the Levenberg–Marquardt function provides the best estimation of the surface roughness after laser treatment compared with the Bayesian-regularization function and the scaled-conjugate-gradient function. The result can be used as a practical prediction and reasonable guideline for the optimization of LMC processes.

中文翻译：

---

用多元线性回归和人工神经网络方法预测陶瓷激光选择性金属化的表面粗糙度

陶瓷激光选择性金属化 (LMC) 已被广泛应用于在陶瓷电路载体上制备高质量的电气图案。作为主导因素，激光处理后的表面粗糙度主要受激光技术参数控制，如激光功率、扫描速度和激光频率。因此，在表面粗糙度和激光参数之间建立准确的关系将对 LMC 试样大有裨益。在目前的研究中，机器学习用于模拟氧化铝-氧化铜陶瓷上的 LMC 过程。通过多元线性回归方法揭示了每个激光参数的影响以及它们之间的相互作用。与贝叶斯正则化函数和缩放共轭梯度函数相比，使用 Levenberg-Marquardt 函数训练的人工神经网络模型提供了对激光处理后表面粗糙度的最佳估计。该结果可用作优化 LMC 工艺的实用预测和合理指南。