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.

翻译：在质量控制中，为确保结构完整性、排除关键体积缺陷的存在并验证目标微观组织的形成，需对微观组织进行严格检测。对于淬火态层级结构钢，贝氏体和马氏体微观组织的形貌是保障材料在服役条件下可靠性的关键因素。因此，工业界通过对材料截面进行小样本金相检测，验证此类微观组织的针状形貌。我们通过循环测试结果表明，尽管人员经过严格培训，这种视觉分级仍存在显著主观性。为此，我们提出一种基于深度学习的图像分类方法，该方法依据微观组织类型区分钢材，并根据ISO 643晶粒度评定标准对其针状长度进行分类。该分类方法可实现层级结构钢的可靠、客观且自动化分类。具体而言，对于马氏体/贝氏体亚型区分和针状长度分类，分别达到了约96%和91%的准确率。这一结果是在包含显著差异和标注噪声的图像数据集上实现的——该数据集由多位金相师（评分员）在十余年间从多个工厂、合金体系、腐蚀剂应用及光学显微镜中采集。可解释性分析揭示了这些模型的决策机制，并为其泛化能力的评估提供了依据。