CAREER: A Multichannel Convolutional Neural Network Framework for Prediction of Damage Nucleation Sites in Microstructure

这一教师早期职业发展(Career)奖支持旨在回答长期存在的问题的研究：材料失败的原因是什么？结构材料是现代生活方式的基石，支持从基础设施到国家安全的各种应用，但它们失败的机制尚不清楚。灾难性失效的第一阶段往往是小空洞的成核，这是一个复杂和多方面的过程，到目前为止还没有得到简化的模型。该项目将利用先进的机器学习方法，可以识别大数据集中的细微趋势，并使用损伤力学模型来揭示气孔成核的细微差别。结果将是一种能够快速筛选材料以确定损伤敏感性的计算机模型。这个项目的教育部分有两个方面。首先，该项目将在基于机器学习的材料力学方面取得进展，初出茅庐的工程师和业余工程师和科学家可以通过一种基于互联网的应用程序来访问，该应用程序名为“Solid Genius”。Solid Genius将免费提供，允许通过用户友好的教育界面直接操作和探索材料模型及其预测能力。其次，该项目将开发一门新的研究生课程，为基于机器学习的损伤机制方面的新兴研究人员提供培训。实验证据表明，晶界是气孔形核的有利位置，但晶界性质与失效可能性之间的有意义的关联尚未确定。计划了一个多通道卷积神经网络(MCCNN)机器学习框架，该框架将能够识别原始微观结构中的潜在气孔形核位置。该框架将同时考虑两种非局部属性(微结构、纹理等)。和局部属性(逐点曲率、倾斜度等)，将它们与训练数据集相结合以产生故障可能性的可靠估计器。训练数据将由重建的实验显微照片和EBSD数据组成，分为“故障”和“无故障”两部分。然后，原始实验数据将通过二次计算和补充的力学模拟来丰富，以提供不可见的通道，如晶界能量和机械应力。然后，训练好的MCCNN框架将与损伤力学模型结合使用，以进一步探索气孔萌生和生长的早期行为。发展将包括强调确定MCCNN模型每个方面的物理可解释性，例如在微观结构中形式化单个卷积层和特征分割之间的联系，以促进该框架在损害评估问题上的更严格应用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) award supports research that will aim to answer the longstanding question: What causes materials to fail? Structural materials are the building blocks of modern lifestyle, supporting applications ranging from infrastructure to national security, yet the mechanisms underlying their failure are not well understood. The first stage of catastrophic failure is often the nucleation of small voids, through a complex and multifaceted process that has so far evaded simplified models. This project will leverage advanced machine learning methods, which can identify subtle trends in large datasets, with damage mechanics models to unravel the nuances of pore nucleation. The result will be a computer model that is able to rapidly screen materials to determine damage susceptibility. The educational part of this project is twofold. First, the project will make advances in machine learning-based mechanics of materials accessible to budding and amateur engineers and scientists through an internet-based application, called “Solid Genius.” Solid Genius will be freely available, allowing direct manipulation and exploration of the material model and its predictive capability through a user-friendly educational interface. Second, the project will develop a new graduate course to provide training for rising researchers in machine learning-based damage mechanics.Experimental evidence indicates that grain boundaries are preferential sites for pore nucleation, but no meaningful correlations between grain boundary properties and failure likelihood have yet been conclusively established. A multi-channel convolutional neural network (MCCNN) machine learning framework is planned that will be able to identify potential pore nucleation sites in pristine microstructure. The framework will simultaneously account for both nonlocal properties (microstructure, grain texture, etc.) and local properties (pointwise curvature, inclination, etc.), synthesizing them against a training dataset to produce a reliable estimator of failure likelihood. Training data will consist of reconstructed experimental micrographs and EBSD data, divided into “failure” and “no-failure” partitions. The raw experimental data will then be enriched with secondary calculations and supplemental mechanics simulations to supply non-visible channels such as grain boundary energy and mechanical stress. The trained MCCNN framework will then be used in concert with a damage mechanics model to further probe the early-time behavior of pore initiation and growth. Development will include an emphasis on determining physical interpretability of each aspect of the MCCNN model, such as formalizing the connection between individual convolutional layers and feature segmentation in microstructure, to facilitate a more rigorous application of the framework to the problem of damage assessment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.