用智能采样与深度网络联合预测3D打印复合材料的多种性能，少实验多精准。
Integrating Wide and Deep Neural Networks with Squeeze-and-Excitation Blocks for Multi-Target Property Prediction in Additively Manufactured Fiber Reinforced Composites
- 结合拉丁超立方采样与注意力机制的宽深神经网络，高效建模多参数影响
- 仅155组实验即实现平均误差12.33%，显著优于其他模型
- 可解释性强，明确关键影响因素，助力材料工艺优化
采用增材制造的连续纤维增强复合材料（CFRC-AM）具备高比强度、轻量化潜力，但其性能对工艺与材料参数交互敏感，全量实验不可行。本研究提出一种数据高效、多输入多输出的学习方法，融合拉丁超立方采样（LHS）实验设计与挤压-激励宽深神经网络（SE-WDNN），基于不同制造参数联合预测CFRC-AM的多项力学与制造性能。使用Markforged Mark Two 3D打印机，在4,320种组合的设计空间中选取并制备了155个样品进行测试，构建模型输入输出数据集。对比了前馈神经网络、Kolmogorov-Arnold网络、XGBoost、CatBoost及随机森林等常用机器学习模型。所提模型在整体测试误差上最低（MAPE = 12.33%），并在多个目标变量上相比基线宽深网络表现显著更优（配对t检验，p ≤ 0.05）。SHAP分析表明，增强策略是影响力学性能的主要因素。结果表明，LHS与SE-WDNN的结合实现了可解释、样本高效的多目标预测，为CFRC-AM参数选择提供了兼顾力学性能与制造指标的决策支持。
原文摘要 · Abstract (English)
Continuous fiber-reinforced composite manufactured by additive manufacturing (CFRC-AM) offers opportunities for printing lightweight materials with high specific strength. However, their performance is sensitive to the interaction of process and material parameters, making exhaustive experimental testing impractical. In this study, we introduce a data-efficient, multi-input, multi-target learning approach that integrates Latin Hypercube Sampling (LHS)-guided experimentation with a squeeze-and-excitation wide and deep neural network (SE-WDNN) to jointly predict multiple mechanical and manufacturing properties of CFRC-AMs based on different manufacturing parameters. We printed and tested 155 specimens selected from a design space of 4,320 combinations using a Markforged Mark Two 3D printer. The processed data formed the input-output set for our proposed model. We compared the results with those from commonly used machine learning models, including feedforward neural networks, Kolmogorov-Arnold networks, XGBoost, CatBoost, and random forests. Our model achieved the lowest overall test error (MAPE = 12.33%) and showed statistically significant improvements over the baseline wide and deep neural network for several target variables (paired t-tests, p <= 0.05). SHapley Additive exPlanations (SHAP) analysis revealed that reinforcement strategy was the major influence on mechanical performance. Overall, this study demonstrates that the integration of LHS and SE-WDNN enables interpretable and sample-efficient multi-target predictions, guiding parameter selection in CFRC-AM with a balance between mechanical behavior and manufacturing metrics.
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