Abstract When it comes to advanced materials for safety-critical applications, the evaluation of the machining-induced microstructural surface integrity represents a primary aspect within the assessment of part quality. Nowadays, presence and extent of machining-induced microstructural anomalies in the workpiece subsurface is manually measured by human inspection of digital micrographs. In the present work, computer-based performance of this task is achieved through a set of algorithms designed to automatically identify microstructural anomalies resulting from material removal operations. Digital surface integrity assessment has been demonstrated with application to scanning electron micrographs exhibiting different levels of microstructural deformation and obtained under different imaging conditions. Furthermore, the digitally detected material condition has been investigated with the support of in-depth field emission gun scanning electron microscopy (FEG-SEM) and electron backscatter diffraction (EBSD) analysis. This has allowed the relationship between the material evidence observed through different strategies to be established. Finally, the set of algorithms has been applied to study the microstructural condition of a large material region, by performing sequential processing of a series of micrographs. In this way, the measurement procedure has been calibrated and its capability to perform surface-integrity evaluation on large areas in an automated and standardised way has been demonstrated.

中文翻译：

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一种自动评估加工引起的微观结构表面完整性的数字方法

摘要 当涉及用于安全关键应用的先进材料时，对加工引起的微观结构表面完整性的评估代表了零件质量评估中的一个主要方面。如今，工件表面下加工引起的微观结构异常的存在和程度是通过人工检查数字显微照片来手动测量的。在目前的工作中，这项任务的基于计算机的性能是通过一组旨在自动识别材料去除操作导致的微观结构异常的算法来实现的。数字表面完整性评估已通过应用于显示不同微观结构变形水平并在不同成像条件下获得的扫描电子显微照片得到证实。此外，在深度场发射枪扫描电子显微镜 (FEG-SEM) 和电子背散射衍射 (EBSD) 分析的支持下，对数字检测的材料条件进行了研究。这使得通过不同策略观察到的物质证据之间的关系得以建立。最后，通过对一系列显微照片进行顺序处理，该组算法已被应用于研究大材料区域的微观结构条件。通过这种方式，测量程序得到了校准，并证明了其以自动化和标准化的方式对大面积进行表面完整性评估的能力。