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The influence of temperature on the deformation behaviour of the C15 CaAl2 Laves phase, a key constituent for enhancing the mechanical properties of Mg alloys up to service temperatures of 200 °C, remains largely unexplored. This study presents, for the first time, the nanoscale brittle-to-ductile transition (BDT) of this intermetallic phase through in situ testing including nanoindentation and micropillar splitting conducted at elevated temperatures. By correlating observations from these techniques, changes in deformation of CaAl2 were identified in relation to temperature. High-temperature nanoindentation quantitatively determined the BDT temperature range, and revealed that CaAl2 undergoes a BDT at ∼0.55 Tm, exhibiting an intermediate region of microplasticity. A noticeable decrease in nanoindentation hardness was observed at ∼450–500 °C, while indentation cracking was not observed above 300 °C. Results from high-temperature micropillar splitting revealed cracking and brittle pillar splitting up to 300 °C, with an increase in apparent fracture toughness from 0.9 ± 0.1 MPa·√m to 2.8 ± 0.3 MPa·√m, and subsequent crack-free plastic deformation from 400 °C. Transmission electron microscopy analysis of the deformed material from nanoindentation revealed that the BDT of CaAl2 may be attributed to enhanced dislocation plasticity with increasing temperature.

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.