Statistical Analyses of Plastic Deformation Events via Computer Vision: Case Study of Additive Manufactured Microstructures

With the ongoing development of high-throughput characterization approaches for metallic materials, full-field measurements and their rapid statistical analysis have become increasingly essential for capturing material heterogeneities and providing a comprehensive understanding of metal processing and microstructure effects. In the context of mechanical properties, understanding the influence of microstructure on the localization of plasticity at small scales is crucial for describing a material’s behavior. While experimental techniques for measuring plasticity continue to improve, there remains a significant need for rapid and statistically robust analysis methods of resulting data. Here, we propose a framework based on computer vision and event merging for the automated extraction of deformation events from high-resolution digital image correlation measurements. Notably, the proposed approach demonstrates versatility, enabling its application with different material systems, microstructure, and deformation temperature conditions ranging from room to elevated temperatures. A case study is presented to statistically evaluate the impact of processing methods, (i.e., wrought vs additive manufacturing), on the distribution of deformation events during monotonic loading of 718 alloy. Significant differences in plastic localization behavior are observed between the wrought and additively manufactured 718 materials. These differences are discussed in light of their microstructural features.