Semi-supervised CAPP Transformer Learning via Pseudo-labeling

• URL: http://arxiv.org/abs/2602.01419v1
• Date: Sun, 01 Feb 2026 19:51:39 GMT
• Title: Semi-supervised CAPP Transformer Learning via Pseudo-labeling
• Authors: Dennis Gross, Helge Spieker, Arnaud Gotlieb, Emmanuel Stathatos, Panorios Benardos, George-Christopher Vosniakos,
• Abstract summary: We propose a semi-supervised learning approach to improve transformer-based CAPP transformer models without manual labeling.<n>An oracle, trained on available transformer behaviour data, filters correct predictions from unseen parts, which are then used for one-shot retraining.
• Score: 3.6799158613885066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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