Semi-supervised CAPP Transformer Learning via Pseudo-labeling
This work addresses data scarcity in manufacturing environments for CAPP, but it is incremental as it builds on existing transformer methods with a semi-supervised adaptation.
The paper tackles the problem of limited dataset availability in high-level Computer-Aided Process Planning (CAPP) by proposing a semi-supervised learning approach that uses pseudo-labeling to improve transformer-based models without manual labeling, resulting in consistent accuracy gains over baselines in experiments on small-scale datasets.
High-level Computer-Aided Process Planning (CAPP) generates manufacturing process plans from part specifications. It suffers from limited dataset availability in industry, reducing model generalization. We propose a semi-supervised learning approach to improve transformer-based CAPP transformer models without manual labeling. An oracle, trained on available transformer behaviour data, filters correct predictions from unseen parts, which are then used for one-shot retraining. Experiments on small-scale datasets with simulated ground truth across the full data distribution show consistent accuracy gains over baselines, demonstrating the method's effectiveness in data-scarce manufacturing environments.