Semi-supervised CAPP Transformer Learning via Pseudo-labeling

TL;DR

This paper introduces a semi-supervised learning method for CAPP transformer models that leverages pseudo-labeling and an oracle to improve accuracy in data-scarce manufacturing scenarios.

Contribution

It presents a novel pseudo-labeling approach with an oracle to enhance transformer-based CAPP models without manual data annotation.

Findings

Consistent accuracy improvements over baseline models.

Effective in small-scale, data-scarce environments.

Demonstrates potential for industrial manufacturing applications.

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.