AI 中文总结
本研究采用定制双分支CNN，结合SEM图像与压缩数据，预测含木质素PU泡沫的力学性能，其R²达0.850-0.91，可解释且减少测试不便。
AI 中文摘要
传统硬质泡沫的生物基替代品因可持续性提升和性能具竞争力，已被证实是优良替代物。但由于其制造工艺复杂且破坏性测试常不可行，本研究采用机器学习方法，探究含木质素的硬质聚氨酯（PU）泡沫的微观结构特征是否可与其力学性能建立关联。研究中考察了不同类型及百分比的木质素基多元醇作为部分多元醇替代品，利用扫描电子显微镜（SEM）图像及对应的压缩力学数据，训练了一个定制的最先进双分支卷积神经网络（CNN），专门用于预测密度、比压缩模量、比屈服强度和比压缩强度。该CNN通过加权多输出损失函数进行优化，取得了优异的预测性能：R²值介于0.850至0.91之间，相关系数高于0.92，同时保持平均绝对误差百分比低于9%。这证明了训练后的网络具备预测及捕捉决定承载响应的形态特征的能力。另一方面，Grad-CAM可视化结果显示，网络将预测重点放在具有物理意义的微观结构区域，如胞壁和支柱节点，这证实所提出的网络可归类为可解释、非破坏性且数据驱动的框架，用于预测和理解生物基PU泡沫的力学行为，从而减少耗时制造和破坏性测试带来的不便。
英文摘要
Bio-based alternatives for conventional rigid foams have proven to be good substituents owing to their enhanced sustainability and competitive performance. However, because their manufacturing processes are complex and destructive testing is often impractical, this study investigates whether microstructural features can be correlated with mechanical properties in lignin-containing rigid polyurethane (PU) foams using machine-learning approaches. Various types and percentages of lignin-based polyols were investigated as a partial polyol replacement. Scanning electron microscopy (SEM) images and corresponding mechanical compression data were used to train a custom state-of-art dual-head convolutional neural network (CNN) targeting the specific prediction of density, specific compression modulus, specific yield strength, and specific compression strength. The CNN was optimized with a weighted multi-output loss function, achieving strong predictive performance R2 values ranging from 0.850 to 0.91 and correlation coefficients above 0.92 while maintaining mean absolute error percentages below 9%. This proves the trained network capability to predict and capture morphological features governing load bearing responses. On the other hand, Grad-CAM visualization revealed that the network focused its predictions on physically meaningful microstructural regions such as cell walls and strut junctions, which confirm that the proposed network can be classified as an interpretable, non-destructive and data-driven framework for predicting and understanding bio-based PU foams mechanical behavior, hence reducing the inconvenience caused by time consuming manufacturing and destructive testing.
