Laser micro-drilling is a significant manufacturing method used to drill precise microscopic holes into metals. Quality inspection of micro-holes is costly and redrilling defective holes can lead to imperfection owing to the misalignment in re-aligning the removed specimens. Thus this paper proposes an in-situ, automatic inspection method using photodiode data and machine learning models to detect defects in real-time during the fabrication of SK5 steel plates with 1064 nm Nd:YAG Laser machines to reduce the workload and increase the quality of products. Further, it explores the possibility of generalizing the models to 51 different scenarios of fabrication by classifying unseen data into 51 classes. A dataset of around 1,500,000 time series data points was generated using an optical probe while drilling over 56,000 holes into test specimens. 15 different combinations of thickness and diameter were drilled using suggested parameters. An additional 12 potential defect-prone conditions were designed to obtain data during conditional drilling. Hole quality was measured for each hole using OGP 3D profile microscope measuring machine. Results showed high accuracy in specialized defect detection within each scenario and showed a possibility of classifying photodiode data patterns, offering opportunities to improve the practicality of the proposed solution.

激光微钻孔是一种重要的制造方法，用于在金属上钻出精确的微观孔。微孔的质量检查成本高昂，并且由于重新对准移除的样本时未对准，重新钻有缺陷的孔可能会导致缺陷。因此，本文提出了一种使用光电二极管数据和机器学习模型的原位自动检测方法，在使用 1064 nm Nd:YAG 激光机制造 SK5 钢板的过程中实时检测缺陷，以减少工作量并提高质量。产品。此外，它还探索了通过将未见过的数据分为 51 个类别，将模型推广到 51 种不同的制造场景的可能性。使用光学探针在测试样本上钻 56,000 多个孔时生成约 1,500,000 个时间序列数据点的数据集。使用建议参数钻孔了 15 种不同的厚度和直径组合。另外还设计了 12 个潜在的缺陷易发条件，以便在条件钻井期间获取数据。使用 OGP 3D 轮廓显微镜测量机测量每个孔的孔质量。结果表明，每种情况下的专门缺陷检测都具有很高的准确性，并显示了对光电二极管数据模式进行分类的可能性，从而为提高所提出的解决方案的实用性提供了机会。