Predicting additive manufacturing defects with robust feature selection for imbalanced data
针对电子束熔融过程中缺陷率低于2%的罕见事件，提出一种结合重采样与集成学习的特征选择算法，在保持精度的前提下将缺陷预测召回率提升43%，适用于制造过程改进。
Promptly predicting defects during an additive manufacturing process using only copious log data provides many advantages, albeit with computational limitations. We focus on predicting defects during electron beam melting with the black box nature of the manufacturing machine. For an accurate prediction of defects, which are rare (<2%), we extract temporal information to track abnormalities and formulate a feature selection algorithm that maximizes the expected value of a cost-sensitive accuracy. Correct identification of features responsible for the defects increases predictive power and informs manufacturers of potential corrective/preventive actions for process improvement. We solve the feature selection through resampling strategies integrated with ensemble procedures to handle data uncertainty and imbalance. Exploiting data uncertainty in our search leads to finding robust features with consistent predictive power. Our proposed methodology shows a 43% improvement in predicting defects (recall) without losing precision. Beyond additive manufacturing, this methodology has general application for rare-event prediction and imbalanced datasets.