工艺与制造英文2026
基于Transformer的深度学习方法预测自动铺纤工艺间隙宽度
Automated Fiber Placement Gap Width Prediction Using a Transformer-Based Deep Learning Approach
Diogo Cardoso, António Ramos Silva, Nuno Correia · INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal
摘要整理
自动铺纤（AFP）是复合材料制造中的关键工艺，精确的纤维束铺放对实现高质量、高性能工程部件至关重要。然而，工艺参数偏差频繁导致间隙和重叠等缺陷，严重威胁结构完整性。虽然存在多种监测技术，但从复杂传感器数据中准确预测和理解缺陷形成机制仍存在挑战。本研究创新性地应用Transformer深度学习架构来增强AFP工艺间隙宽度的估计精度。基于公开工业AFP数据集，该方法采用定制化位置编码方案，有效融合纤维束铺放工艺的关键空间信息。模型预测性能优异，平均绝对百分比误差（MAPE）达1.04%，决定系数（R²）为0.9143，展现出卓越的间隙宽度估计能力。进一步采用SHAP（SHapley加法解释）分析方法，深入评估制造工艺变量间的复杂相互作用。本研究确立了Transformer架构作为AFP工艺监测的有前景且可解释的数据驱动工具的地位，为基于注意力机制的虚拟计量学提供了概念验证，为深化工艺理解和缺陷预防指明了发展方向。
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