Deep channel fabrication on copper by multi-scan underwater laser machining
Deep channel fabrication on copper by multi-scan underwater laser machining
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DOI：
10.1016/j.optlastec.2018.10.046
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发表时间：
2019-04
影响因子：
5
通讯作者：
Wenhe Feng;Jiang Guo;Wenjin Yan;Y. Wan;Hongyu Y Zheng
中科院分区：
文献类型：
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作者：
Wenhe Feng;Jiang Guo;Wenjin Yan;Y. Wan;Hongyu Y Zheng
纳秒脉冲范围内的激光加工利用激光辐射引起的热来烧蚀固体材料，然而，热驱动烧蚀通常伴随着诸如氧化、碎片重铸和毛刺形成的不利影响。一种有效的技术发明，以尽量减少这些影响是水下激光加工。在这项研究中，我们提出了一个多扫描激光加工过程中的铜样品浸泡在流动的水与安全的上表面。结果表明，该工艺可获得热影响区小、重铸碎屑少、毛刺少的通道。通过应用多扫描加工，可以生产出深度高达275 µm、锥度角为13-22°、粗糙度为1.30-6.95 µm的通道。利用多项式回归（PR）分析和高斯过程回归（GPR）机器学习算法，建立了处理参数与信道特性之间的经验模型，并验证了它们在信道特性预测中的性能。
Laser machining in the nanosecond pulse regime utilises the heat induced by laser irradiation to ablate solid material, however the heat-driven ablation is very often accompanied with adverse effects such as oxidation, debris recast and burr formation. An effective technique invented to minimise these effects was underwater laser machining. In this study, we present a multi-scan laser machining process on a copper sample immersed under flowing water with a secured upper surface. The results show that channels with smaller heat affected zone (HAZ), less debris recast and minimal burr can be produced by this process. By applying multi-scan machining, channels with up to 275 µm depth, 13–22° taper angle and 1.30–6.95 µm roughnessSawere produced. Moreover, empirical models relating the processing parameters to channels’ characteristics were derived using polynomial regression (PR) analysis and a machine learning algorithm, Gaussian process regression (GPR), after which their performances in the prediction of the channels’ characteristics were validated.