AI 中文总结
针对传统模型瓶颈及去噪扩散模型训练数据量大的问题，提出连续条件去噪扩散模型，通过邻域损失训练策略等，在紧凑数据集上学习微观结构统计模式，成功为低碳钢生成相关微观结构，助力材料工艺设计优化。
AI 中文摘要
传统计算模型在模拟微观结构演变时存在计算瓶颈。生成式机器学习方法如去噪扩散模型可用于过程 - 结构映射的替代建模，但训练常需大量数据。为此提出一种连续条件去噪扩散模型，在紧凑数据集上训练，先对微观结构图像加噪，再训练神经网络去噪以学习统计模式。还提出邻域损失训练策略解决连续值过程条件的数据低效问题。该模型成功为低碳钢生成了基于锰成分的代表性微观结构，为材料及其微观结构的高效工艺设计和优化开辟了道路。
英文摘要
Traditional computational models, such as cellular automata and phase-field methods, are effective for simulating microstructural evolution but often face computational bottlenecks, limiting their application in high-throughput and on-demand process optimization. Generative machine learning approaches, such as denoising diffusion models, have emerged as powerful tools for surrogate modeling of process-structure maps, specifically producing representative microstructures conditioned on process parameters. However, they often require large amounts of data for training, particularly when process conditions are continuous rather than discrete categorical variables. To address this, we present a continuous conditional denoising diffusion model for generating microstructures conditioned on processing parameters. Trained on a compact dataset of process-microstructure pairs, this framework first adds noise to microstructure images and then trains a neural network to progressively remove the noise, learning the underlying statistical patterns of the microstructure. To address data inefficiencies associated with continuously valued process conditions, we propose a vicinal-loss training strategy that associates process conditions in data-sparse regions with nearby conditions in the dataset. Combined with classifier-free guidance and denoising diffusion implicit sampling, this approach enables data-efficient continuous conditional generation of microstructures compared to classical denoising diffusion models. The model successfully generates representative microstructures for low-carbon steel conditioned on manganese composition, matching key physical features such as phase and grain morphology, grain size distribution, phase fraction, and interfacial area distribution. More generally, this approach opens avenues for efficient process design and optimization of materials and their microstructures.
Comments42 pages, 22 figures
