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- W2892870023 abstract "High performance materials, from natural bone over ancient damascene steel to modern superalloys, typically possess a complex structure at the microscale. Their properties exceed those of the individual components and their knowledge-based improvement therefore requires understanding beyond that of the components' individual behaviour. Electron microscopy has been instrumental in unravelling the most important mechanisms of co-deformation and in-situ deformation experiments have emerged as a popular and accessible technique. However, a challenge remains: to achieve high spatial resolution and statistical relevance in combination. Here, we overcome this limitation by using panoramic imaging and machine learning to study damage in a dual-phase steel. This high-throughput approach not only gives us strain and microstructure dependent insights across a large area of this heterogeneous material, but also encourages us to expand current research past interpretation of exemplary cases of distinct damage sites towards the less clear-cut reality." @default.
- W2892870023 created "2018-10-05" @default.
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- W2892870023 date "2018-09-25" @default.
- W2892870023 modified "2023-09-27" @default.
- W2892870023 title "High-resolution, yet statistically relevant, analysis of damage in DP steel using artificial intelligence" @default.
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