Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning

The digitization of materials is the prerequisite for accelerating product development. However, technologically, this is only beneficial when reliability is maintained. This requires comprehension of the microstructure-driven fatigue damage mechanisms across scales. A substantial fraction of the lifetime for high performance materials is attributed to surface damage accumulation at the microstructural scale (e.g., extrusions and micro crack formation). Although, its modeling is impeded by a lack of comprehensive understanding of the related mechanisms. This makes statistical validation at the same scale by micromechanical experimentation a fundamental requirement. Hence, a large quantity of processed experimental data, which can only be acquired by automated experiments and data analyses, is crucial. Surface damage evolution is often accessed by imaging and subsequent image post-processing. In this work, we evaluated deep learning (DL) methodologies for semantic segmentation and different image processing approaches for quantitative slip trace characterization. Due to limited annotated data, a U-Net architecture was utilized. Three data sets of damage locations observed in scanning electron microscope (SEM) images of ferritic steel, martensitic steel, and copper specimens were prepared. In order to allow the developed models to cope with material-specific damage morphology and imaging-induced variance, a customized augmentation pipeline for the input images was developed. Material domain generalizability of ferritic steel and conjunct material trained models were tested successfully. Multiple image processing routines to detect slip trace orientation (STO) from the DL segmented extrusion areas were implemented and assessed. In conclusion, generalization to multiple materials has been achieved for the DL methodology, suggesting that extending it well beyond fatigue damage is feasible.

Zigzag Dissociation Mode of 〈c + a〉 Dislocations on the 101¯1 Plane in Magnesium Alloys

Fundamental understanding of the dissociation mode of 〈c + a〉 dislocations on the 101¯1 plane is required before the goal of improving the ductility of Mg alloys attained. In this study, our density-functional theory calculations reveal that the atoms in the 101¯1 plane slip along a zigzag trace through a low-energy pathway. We thus propose a novel zigzag dissociation mode based on this slip trace. In particular, the shuffling motion of atoms is observed at the position of stable stacking fault, which is closely related to the c/a ratio of the hexagonal closed-packed lattices.