Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects such as keyhole, balling, and lack of fusion can significantly compromise structural integrity. This study presents SL-RF+ (Sequentially Learned Random Forest with Enhanced Sampling), a novel Sequential Learning (SL) framework for melt pool defect classification designed to maximize data efficiency and model accuracy in data-scarce environments. SL-RF+ utilizes RF classifier combined with Least Confidence Sampling (LCS) and Sobol sequence-based synthetic sampling to iteratively select the most informative samples to learn from, thereby refining the model's decision boundaries with minimal labeled data. Results show that SL-RF+ outperformed traditional machine learning models across key performance metrics, including accuracy, precision, recall, and F1 score, demonstrating significant robustness in identifying melt pool defects with limited data. This framework efficiently captures complex defect patterns by focusing on high-uncertainty regions in the process parameter space, ultimately achieving superior classification performance without the need for extensive labeled datasets. While this study utilizes pre-existing experimental data, SL-RF+ shows strong potential for real-world applications in pure sequential learning settings, where data is acquired and labeled incrementally, mitigating the high costs and time constraints of sample acquisition.

翻译：确保金属增材制造（MAM）部件的质量和可靠性至关重要，尤其是在激光粉末床熔融（L-PBF）过程中，诸如匙孔、球化及未熔合等熔池缺陷会严重损害结构完整性。本研究提出了SL-RF+（采用增强采样的序贯学习随机森林），这是一种新颖的用于熔池缺陷分类的序贯学习（SL）框架，旨在数据稀缺环境下最大化数据效率和模型精度。SL-RF+利用RF分类器结合最小置信度采样（LCS）和基于Sobol序列的合成采样，迭代地选择信息量最大的样本进行学习，从而以最少的标注数据细化模型的决策边界。结果表明，SL-RF+在准确率、精确率、召回率和F1分数等关键性能指标上均优于传统机器学习模型，证明了其在有限数据下识别熔池缺陷方面具有显著的鲁棒性。该框架通过聚焦于工艺参数空间中的高不确定性区域，有效捕获了复杂的缺陷模式，最终无需大量标注数据集即可实现卓越的分类性能。虽然本研究利用了预先存在的实验数据，但SL-RF+在纯序贯学习场景（即数据被增量式获取和标注）中显示出强大的实际应用潜力，从而缓解了样本获取的高成本和时间限制。