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.
@article{arxiv.2602.01419,
title = {Semi-supervised CAPP Transformer Learning via Pseudo-labeling},
author = {Dennis Gross and Helge Spieker and Arnaud Gotlieb and Emmanuel Stathatos and Panorios Benardos and George-Christopher Vosniakos},
journal= {arXiv preprint arXiv:2602.01419},
year = {2026}
}