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.

本文针对金属学家对钢材织构的视觉分类存在主观性强的问题，提出了一种基于深度学习的钢材组织类型及其指针长度自动分类方法，达到了 96% 的准确率，从而提高了对钢材组织的客观性和可靠性。

利用深度学习进行钢铁微结构质量控制

Microstructure quality control of steels using deep learning

The inner structure of a material is called microstructure. It stores the
genesis of a material and determines all its physical and chemical properties.
While microstructural characterization is widely spread and well known, the
microstructural classification is mostly done manually by human experts, which
gives rise to uncertainties due to subjectivity. Since the microstructure could
be a combination of different phases or constituents with complex substructures
its automatic classification is very challenging and only a few prior studies
exist. Prior works focused on designed and engineered features by experts and
classified microstructures separately from the feature extraction step.
Recently, Deep Learning methods have shown strong performance in vision
applications by learning the features from data together with the
classification step. In this work, we propose a Deep Learning method for
microstructural classification in the examples of certain microstructural
constituents of low carbon steel. This novel method employs pixel-wise
segmentation via Fully Convolutional Neural Networks (FCNN) accompanied by a
max-voting scheme. Our system achieves 93.94% classification accuracy,
drastically outperforming the state-of-the-art method of 48.89% accuracy.
Beyond the strong performance of our method, this line of research offers a
more robust and first of all objective way for the difficult task of steel
quality appreciation.

通过基于深度学习和像素分割方法的微结构分类模型，成功实现了对于低碳钢微观组织多个组分类型的快速、精确分类，准确率达到了 93.94%，比现有技术提升明显，为钢质检测等领域提供了更加客观、准确的检测方法。