English(EN) Semi-supervised CAPP Transformer Learning via Pseudo-labeling

新的半监督学习在数据稀疏环境下提升CAPP Transformer模型性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种新颖的半监督学习方法，以增强基于Transformer的计算机辅助工艺规划（CAPP）模型，特别是在数据稀缺的制造环境中。该方法利用一个“神谕”来过滤未见过零件的准确预测，然后用于重新训练，从而在无需大量手动标注的情况下提高模型泛化能力。实验表明，与基线方法相比，准确性持续提高，凸显了该技术在改进CAPP Transformer模型方面的有效性。 AI

影响这项研究有望提高数据有限的行业中制造工艺规划的效率和准确性。

排序理由该集群包含一篇详细介绍新机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Dennis Gross, Helge Spieker, Arnaud Gotlieb, Emmanuel Stathatos, Panorios Benardos, George-Christopher Vosniakos · 2026-09-01 04:00

伪标签半监督CAPP Transformer学习

arXiv:2602.01419v2 Announce Type: replace-cross 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-s…

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伪标签半监督CAPP Transformer学习

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