Multiclass Defect Classification from Legacy Foundry Data: A Decision Support System for Reducing Manual Inspection Time

This paper presents a machine learning-based decision support system for multiclass defect detection, utilizing exclusively heterogeneous legacy process data to minimize manual inspection time in foundries. Validated on 51,377 products across 193 defect categories, the methodology resolves structural data inconsistencies through k-nearest neighbor (kNN) imputation and piecewise winsorization. A multi-stage feature selection cascade, incorporating variance thresholding, correlation filtering, and Random Forest Feature Importance (RFFI), reduces the feature space from 139 to 78 process-critical variables. Following Synthetic Minority Over-sampling Technique (SMOTE)-based class balancing, five classifiers were benchmarked via 10-fold cross-validation and optimized using the macro-averaged F3-score to mathematically penalize undetected defects. Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) provided superior predictive baselines. To enforce strict zero-defect constraints, an asymmetric risk function shifted decision boundaries, enabling the risk-calibrated LightGBM model to reduce manual inspection volume by 9.72% with zero defect escapes. For resolving conflicting predictions, multi-algorithm decision fusion was implemented. By statistically evaluating the joint probabilities of the base models’ post-calibration outputs, a Naive Bayes Stacking meta-classifier effectively neutralizes single-algorithm inductive biases. Ultimately, synthesizing these F3-optimized, risk-calibrated base models via meta-learning successfully isolated true defect-free components, maximizing the final inspection time reduction to 14.82% while strictly maintaining zero defect escapes.