用伪标签提升小数据下的智能工艺规划模型性能。
Semi-supervised CAPP Transformer Learning via Pseudo-labeling
- 利用已有模型行为数据构建筛选器，生成高质量伪标签。
- 在小规模数据集上实现比基线更高的准确率，验证有效性。
- 适合制造业中数据稀缺但需快速迭代的场景。
高层计算机辅助工艺规划（CAPP）从零件规格生成制造工艺方案，但工业界数据集有限，影响模型泛化能力。本文提出一种半监督学习方法，无需人工标注即可改进基于Transformer的CAPP模型。通过在已有数据上训练一个筛选器（oracle），从新零件的预测中选出可信结果，用于单次重训练。在包含模拟真实分布的全数据范围的小规模数据集上实验显示，该方法持续优于基线，证明其在数据匮乏的制造环境中具有显著效果。
原文摘要 · Abstract (English)
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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