Knut Marthinsen

We present kikuchipy, an open-source toolbox for analysis of electron backscatter diffraction patterns, written in Python. The software is capable of both Hough and dictionary indexing and orientation and/or projection center refinement of patterns stored in file formats from all major vendors. Indexing results can be validated using maps independent of indexing and by visually comparing experimental and simulated patterns. By leveraging scientific packages in the Python ecosystem, emphasis is put on making the indexing workflow flexible and improve results through fast iteration. The software’s capabilities are demonstrated on three application examples: analysis of orientation relationships in a super duplex stainless steel, phase differentiation of aluminum and silicon in a cast modified Al–Si alloy, and phase differentiation of particles in an Al–Mn alloy as Al6Mn or α–AlMnSi. The diffraction patterns and analysis workflows are made publicly available. kikuchipy was created and is developed as a resource for the electron microscopy community, allowing anyone to improve the software or include it into their own analysis workflows or softwares.

In the present work artificial neural networks (ANN) models have been implemented and trained as surrogate models to replicate two physics-based microstructure models for Al-alloys, i.e. the ALFLOW model, which predicts the sub-structure evolution and associated flow stress during plastic deformation and the softening model ALSOFT, which predicts the softening behavior after hot/cold deformation, in view of the combined effect of recovery and recrystallization. Input for both ANN models was limited to variables such as strain, strain rate, time, temperature and solute concentration, and the flow stress as the output. Accuracy and efficiency were tested for different ANN architectures. It is demonstrated that fully connected feed-forward neural network architectures with ∼3 hidden layers are suitable as surrogate models for both ALFLOW and ALSOFT, with a potential speed-up of ∼100x for ALFLOW and ∼10x for ALSOFT.

In the present work, the goal is to use two-scale simulations to be incorporated into the full-field open software DAMASK version 2.0.3 crystal plasticity framework, in relation to the Bauschinger effect caused by the composite effect of the presence of second-phase particles with surrounding deformation zones. The idea is to achieve this by including a back stress of the critical resolved shear stress in a single-phase simulation, as an alternative to explicitly resolving the second-phase particles in the system. The back stress model is calibrated to the volume-averaged behaviour of detailed crystal plasticity simulations with the presence of hard, non-shearable spherical particles or voids. A simplified particle-scale model with a periodic box containing only one of the spherical particles in the crystal is considered. Applying periodic boundary conditions corresponds to a uniform regular distribution of particles or voids in the crystal. This serves as an idealised approximation of a particle distribution with the given mean size and particle volume fraction. The Bauschinger effect is investigated by simulating tensile–compression tests with 5% and 10% volume fractions of particles and with 1%, 2%, and 5% pre-strain. It is observed that an increasing volume fraction increases the Bauschinger effect, both for the cases with particles and with voids. However, increasing the pre-strain only increases the Bauschinger effect for the case with particles and not for the case with voids. The model with back stress of the critical resolved shear stress, but without the detailed particle simulation, can be fitted to provide reasonably close results for the volume-averaged response of the detailed simulations.

For Al-Mg-Si alloys, cooling after homogenisation is a crucial step because the precipitation of the equilibrium β-Mg₂Si phase determines the processing capabilities in subsequent steps, as well as the subsequent precipitation age hardening potential, and thus, the final properties. It is therefore important to understand how microstructural variations affect the transformation of β-Mg₂Si during cooling after homogenisation. In the present work, alloys with similar effective solute contents of Mg and Si, but with different microstructures and a different amount of primary Al-Fe-Si phases, were produced. Characterisation of the precipitation reaction was performed using interrupted quench experiments with cooling rates of 1–6 K/min, monitored by light optical microscopy (LOM), scanning electron microscopy (SEM) and conductivity measurements. Precipitation kinetics for β-Mg₂Si was found to increase in microstructures with shorter secondary dendrite arm spacing (DAS). However, despite measuring both a higher density and volume fraction of the primary phases, no effect on the phase transformation from an increased iron content was found in terms of precipitation kinetics or particle count statistics. Furthermore, comparisons with iron-free high-purity-based alloys revealed that the precipitation reaction for β-Mg₂Si was identical in the two different microstructures both in terms of onset temperature and overall kinetics. The present results show that nucleation of β-Mg₂Si is not dependent on the larger constituent phases and indicates that overall transformation kinetics is governed by bulk diffusion rates.

Quantitative Convergent Beam Electron Diffraction (QCBED) is used to determine selected structure factor amplitudes as well as three-phase structure invariants in non-centrosymmetric InP. The method used is based on center disk intensities only. Particular parameter sensitive pixels in the pattern are identified theoretically. With this selected area QCBED method, structure invariants can be determined with an accuracy of a few degrees and the structure factor amplitudes with an accuracy better than 0.5%.