Deep-learning assisted damage observations on the microscale – A new viewpoint on microstructural deformation, fracture and decohesion processes

In recent years, state-of-the-art micromechanical systems have given researchers the ability to observe deformation processes in-situ. While this technology enables a site-specific observation, this very achievement can turn into a major limitation: To deduct conclusions about the relevance of specific processes for the bulk material, a larger field of view than typically possible in microscale observations is often required. Please click Additional Files below to see the full abstract

Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning.

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 now gives us strain and microstructure dependent insights into the prevalent damage mechanisms and allows the separation of inclusions and deformation-induced damage across a large area of this heterogeneous material. Aiming for the first time at automated classification of the majority of damage sites rather than only the most distinct, the new method also encourages us to expand current research past interpretation of exemplary cases of distinct damage sites towards the less clear-cut reality

On the damage behaviour in dual-phase DP800 steel deformed in single and combined strain paths

Ductile damage in dual-phase steels deteriorates the mechanical properties, resulting in a lowered crashworthiness and a shorter life-time under cyclic loading. However, most works focus on the observation of damage under simple strain paths, even though work pieces are often subjected to combined strain paths during forming. In this work, we set out to characterise the damage behaviour for a DP800 steel subjected to combinations of tensile and bending loads. To this end, we use an automated approach based on machine learning to quantify damage in high-resolution SEM panoramic images, coupled with finite element modelling to determine the stress state, and high-throughput nanoindentation to determine the strain hardening behaviour. We find a strong connection between loading direction and damage formation in both steps of the forming process. By focussing on different possible combinations of positive and negative triaxiality, we show that the combination of two deformation modes with positive triaxiality leads to a promotion of damage formation. A negative triaxiality applied after a positive triaxiality leads to void closure, reducing the number of damage sites and total void area. Importantly, deformation with negative triaxiality carried out before deformation with positive triaxiality inhibits damage nucleation and growth due to strain hardening

GOCO05c: A New Combined Gravity Field Model Based on Full Normal Equations and Regionally Varying Weighting

GOCO05c is a gravity field model computed as a combined solution of a satellite-only model and a global data set of gravity anomalies. It is resolved up to degree and order 720. It is the first model applying regionally varying weighting. Since this causes strong correlations among all gravity field parameters, the resulting full normal equation system with a size of 2 TB had to be solved rigorously by applying high-performance computing. GOCO05c is the first combined gravity field model independent of EGM2008 that contains GOCE data of the whole mission period. The performance of GOCO05c is externally validated by GNSS–levelling comparisons, orbit tests, and computation of the mean dynamic topography, achieving at least the quality of existing high-resolution models. Results show that the additional GOCE information is highly beneficial in insufficiently observed areas, and that due to the weighting scheme of individual data the spectral and spatial consistency of the model is significantly improved. Due to usage of fill-in data in specific regions, the model cannot be used for physical interpretations in these regions