| 肖庆晖,张仁嘉,刘士杰,胡文轩,吕晨晞,朱思瑛,易敏.增材铜合金拉伸力学行为的卷积神经网络预测[J].计算力学学报,2024,41(5):843~850 |
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| 增材铜合金拉伸力学行为的卷积神经网络预测 |
| Predicting tensile behavior of additively manufactured copper alloys by convolutional neural network |
| 投稿时间：2024-07-22 修订日期：2024-08-13 |
| DOI：10.7511/jslx20240722002 |
| 中文关键词: 增材制造 卷积神经网络 晶体塑性 铜合金 |
| 英文关键词:additive manufacturing convolutional neural networks crystal plasticity copper alloys |
| 基金项目:国家重点研发青年科学家项目(2022YFB4600700);国家科技重大专项(J2019-IV-0014-0082);国家青年人才项目资助. |
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| 中文摘要: |
| 深度学习因其在处理复杂数据和解决复杂问题方面的显著优势而备受关注,已应用于材料性能预测领域。本文提出了一种结合卷积神经网络模型与晶体塑性有限元方法的预测框架,在晶体塑性模型中考虑了增材铜合金(CuCrZr)固溶强化、位错强化以及晶界强化的贡献,实现了基于增材铜合金的晶体学织构极图、微结构图和晶粒尺寸预测单轴拉伸力学行为的目标。首先,基于实验结果对晶体塑性模型参数进行校正,验证了模型的准确性及预测能力。随后,使用参数校正后的晶体塑性模型对增材铜合金不同的代表体积元进行了一系列的有限元模拟,并利用这些模拟结果对卷积神经网络模型进行了训练、验证与测试。研究结果表明,该卷积神经网络模型在保证预测精度的同时,显著减少了计算时间,展示了其在增材铜合金力学性能预测方面的应用前景。 |
| 英文摘要: |
| Deep learning has gained significant attention due to its remarkable advantages in handling complex data and tasks,and has been successfully applied in material property prediction.Here,a mathematical framework combining the convolutional neural network (CNN) model with the crystal plasticity finite element method (CPFEM) that considers the strengthening contributions from solid solution,dislocation and grain boundary is proposed to predict the uniaxial tensile mechanical behavior of additively manufactured CuCrZr copper alloy by using its crystallographic texture polar figure,microstructure figure and grain size.The crystal plasticity model parameters are calibrated by using experimental results to verify the model’s accuracy and predictive ability.Subsequently,a series of CPFEM simulations is conducted for different representative volume elements using the calibrated crystal plasticity model.These simulation results are used to train,validat,and test the CNN model.The results show that the CNN model significantly reduces the computation time while guaranteeing the prediction accuracy,demonstrating its promising application in mechanical property prediction. |
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