面向拓扑优化应用的机器学习方法用于表征熔融沉积成型材料的不确定弹性能
Machine learning for characterizing uncertain elastic properties of fused filament fabricated materials for topology optimization applications
AI总结：
本文提出一种神经网络模型，利用DIC表面应变场量化FFF打印件块体与熔合层弹性模量的随机分布，并在合成数据上验证了高精度预测能力。
摘要：
熔融沉积成型（FFF）中采用的分层方法能够制造由拓扑优化生成的复杂设计。与逐层工艺相关的缺陷给打印件的局部弹性模量引入了显著的随机变异性。连接块体材料的熔合层沿线的弹性模量与块体区域的弹性模量不同。对这两个区域的变异进行准确定量测量，对于实现稳健的优化设计至关重要。本研究旨在根据通过数字图像相关（DIC）测量的打印件表面应变场，量化随机分布的参数。均值和标准差这两个统计特性足以表征每个区域中的随机弹性模量场。开发了一种高效的神经网络模型，用于估计块体层和熔合层内局部弹性模量的空间变化。该模型在一个具有已知弹性模量场分布的合成应变场数据集上进行训练。只要标准差低于随机场均值的60%，该模型就能很好地建立弹性模量与输入应变之间的相关性。该模型在测试数据上的预测精度（以R2分数衡量）对于熔合材料的均值和标准差分别为0.99和0.95。块体材料的分数各为0.97。将训练好的模型应用于预测在打印速度为30 mm/s、挤出温度为493.15 K条件下打印的FFF材料，基于其DIC测量的表面应变数据，预测其弹性模量分布。模型预测块体材料的均值和标准差分别为1.2 GPa和1 GPa，熔合区域为400 MPa和430 MPa。预测结果的验证证实了该方法在测量打印件局部性能不确定性方面的可靠性。
英文摘要：
The layering approach used in fused filament fabrication (FFF) enables creation of complex designs generated by topology optimization. Defects associated with the layer-by-layer process, introduce considerable random variability to the local elastic modulus of the print. The elastic modulus along the fusion layers connecting bulk materials differs from that of the bulk areas. Accurate quantitative measurements of variations in both areas are essential to achieve robust optimized designs. This study aims to quantify the parameters of the random distributions given the surface strain field of the print measured by digital image correlation (DIC). Two statistical properties, mean and standard deviation, are sufficient to characterize the stochastic elastic modulus fields in each region. An efficient neural network model is developed to estimate spatial variations in the local elastic modulus within both bulk and fusion layers. This model is trained on a dataset of synthetic strain fields with known distributions in the elastic modulus fields. It performs well in correlating the elastic modulus with the input strain, as long as the standard deviation is below 60% of the mean of the random field. The predictive accuracy of the model on testing data, measured by the R2 score, is 0.99 and 0.95 for mean and standard deviation in the fusion material. The scores for the bulk material are 0.97 each. The trained model is implemented to predict the elastic modulus distribution of an FFF-printed material at a print speed of 30 mm/s and an extrusion temperature of 493.15 K, based on its DIC-measured surface strain data. The model predicts a mean and standard deviation of 1.2 GPa and 1 GPa for the bulk material and 400 MPa and 430 MPa for the fusion region. Validation of predictions confirms the reliability of this approach in measuring uncertainty in the local properties of the prints.
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