Title: Microstructure quality control of steels using deep learning Show Chinese title

Title: 基于深度学习的钢材显微组织质量控制

Abstract: In quality control, microstructures are investigated rigorously to ensure structural integrity, exclude the presence of critical volume defects, and validate the formation of the target microstructure. For quenched, hierarchically-structured steels, the morphology of the bainitic and martensitic microstructures are of major concern to guarantee the reliability of the material under service conditions. Therefore, industries conduct small sample-size inspections of materials cross-sections through metallographers to validate the needle morphology of such microstructures. We demonstrate round-robin test results revealing that this visual grading is afflicted by pronounced subjectivity despite the thorough training of personnel. Instead, we propose a deep learning image classification approach that distinguishes steels based on their microstructure type and classifies their needle length alluding to the ISO 643 grain size assessment standard. This classification approach facilitates the reliable, objective, and automated classification of hierarchically structured steels. Specifically, an accuracy of 96% and roughly 91% is attained for the distinction of martensite/bainite subtypes and needle length, respectively. This is achieved on an image dataset that contains significant variance and labeling noise as it is acquired over more than ten years from multiple plants, alloys, etchant applications, and light optical microscopes by many metallographers (raters). Interpretability analysis gives insights into the decision-making of these models and allows for estimating their generalization capability. Show Chinese abstract

Abstract: 在质量控制中，显微组织被严格研究以确保结构完整性，排除关键体积缺陷的存在，并验证目标显微组织的形成。 对于淬火的、具有分层结构的钢，贝氏体和马氏体显微组织的形态是主要关注点，以保证材料在使用条件下的可靠性。 因此，工业界通过金相学家对材料横截面进行小样本检查，以验证此类显微组织的针状形态。 我们展示了循环测试结果，揭示了尽管人员经过充分培训，这种视觉分级仍受到明显主观性的影响。 相反，我们提出了一种深度学习图像分类方法，该方法根据显微组织类型区分钢，并根据ISO 643晶粒度评估标准对针状长度进行分类。 这种分类方法促进了分层结构钢的可靠、客观和自动分类。 具体而言，分别达到了96%和约91%的准确率，用于区分马氏体/贝氏体亚型和针状长度。 这是在包含显著差异和标注噪声的图像数据集上实现的，因为该数据集是在超过十年的时间内，从多个工厂、合金、腐蚀剂应用和光学显微镜中，由许多金相学家（评分者）采集的。 可解释性分析提供了对这些模型决策过程的见解，并允许估计其泛化能力。