Lower Bainite (LB) and Tempered Martensite (TM) are two common microstructures in modern high-strength steels. LB and TM can render similar mechanical properties for steels, yet LB is often considered superior to TM in resistance to hydrogen embrittlement. Such performance difference has conventionally been attributed to their distinction in certain microstructural features, particularly carbides. The present study developed, MatSegNet, a new contour-aware deep learning (DL) architecture. It is tailored for comprehensive segmentation and quantitative characterization of carbide precipitates with complex contours in high-strength steels, shown to outperform existing state-of-the-art DL architectures. Based on MatSegNet, a high-throughput DL pipeline has been established for precise comparative carbide analysis in LB and TM. The results showed that statistically the two microstructures exhibit similarity in key carbide characteristics with marginal difference, cautioning against the conventional use of carbide orientation as a reliable means to differentiate LB and TM in practice. Through MatSegNet, this work demonstrated the potential of DL to play a critical role in enabling accurate and quantitative microstructure characterization to facilitate development of structure-property relationships for accelerating materials innovation.

翻译：下贝氏体（LB）和回火马氏体（TM）是现代高强度钢中两种常见的显微组织。LB和TM可使钢具有相似的力学性能，但在抗氢脆能力方面，LB通常被认为优于TM。这种性能差异传统上归因于它们在特定显微组织特征（尤其是碳化物）上的区别。本研究开发了MatSegNet，一种新的轮廓感知深度学习（DL）架构。该架构专为高强度钢中具有复杂轮廓的碳化物析出相的全自动分割与定量表征而设计，结果证明其性能优于现有的先进DL架构。基于MatSegNet，建立了一条高通量DL管线，用于LB和TM中碳化物的精确对比分析。结果表明，在统计学上，这两种显微组织在关键碳化物特征上表现出相似性，差异甚微，这警示我们在实践中不宜再将碳化物取向作为区分LB和TM的可靠常规手段。通过MatSegNet，本研究展示了DL在实现精确、定量显微组织表征方面发挥关键作用的潜力，从而有助于构建结构-性能关系，加速材料创新。