Interfaces and Magnetisation Reversal in MnAl-C

Permanent magnets are key elements of modern society. Important application areas are energy conversion including eco-efficient transport, hydro- and wind power. A promising magnetic material is MnAl-C. Although it contains no ferromagnetic elements such as iron, nickel or cobalt, the so-called tau-MnAl-C is ferromagnetic up to high temperatures and has all properties which are prerequisites for high performance permanent magnets. The tau- MnAl-C contains no critical elements and therefore the long term use of this material is environmentally sustainable, in stark contrast to that of rare earth magnets such as Nd-Fe-B. In addition, tau-MnAl-C has a low physical density, which is a significant advantage for transport and aerospace applications. One prerequisite for a good permanent magnet is a high anisotropy: The magnetic moments prefer to be oriented along a certain crystallographic direction. Although the anisotropy of tau-MnAl-C is high for a material without rare earth elements, the resistance to magnetisation reversal, known as coercivity, which has been achieved in practice, is only about 10% of the maximum possible value given by the crystalline anisotropy. This is currently the barrier to the application of MnAl-C as a permanent magnet and can be explained by the microstructure of the material. It contains a range of internal interfaces such as grain and twin boundaries. Previous studies have indicated that these interfaces play a role in the process of magnetisation reversal but the occurring mechanisms and the relative strength of these effects are completely unknown. Understanding the effect of the various interfacial types on the process of magnetisation reversal is the key step in improving the performance of MnAl-C magnets. It will allow researchers and the magnets industry to develop novel processing routes which promote the formation of interfaces which have a beneficial effect on coercivity and supress those which are deleterious. In this project, a novel approach combining state of the art characterisation techniques with cutting edge computer simulations will be used to obtain quantitative information concerning the effect of interfaces on magnetisation reversal in tau-MnAl-C. Previously only small areas of the microstructure have been analysed. In this project electron backscatter diffraction will be used to analyse large areas, yielding accurate populations of the various interface types for the first time. Nanoscale features will be investigated using high resolution transmission electron microscopy. Computer models will be generated from the microstructure data, enabling areas of the microstructure to be reproduced and the effect of interfaces on magnetisation reversal to be computed directly. The application of this approach to magnetic materials is highly novel. Materials with different populations of interfaces will be prepared and studied using this combined method.

Eco-friendly transportation and energy applications require a large amount of high performance permanent magnets. In this project we analyzed a very promising alloy of manganese, aluminum and carbon, MnAl-C, which contains no critical elements in terms of supply and sustainability. The material has highly suitable magnetic properties, yet current developed magnets are far below their potential. Various defects in the microstr ucture negatively affect its magnetic properties. Understanding the influence of the various defect types on the process of magnetization reversal is the key step in improving the performance of MnAl-C magnets. It provides design guidelines for researchers and industry to develop novel processing routes. A new approach combining state of the art characterization techniques with cutting edge computer simulations has been developed to quantify the effect of defects on the MnAl-C magnets performance. Large areas of the material were investigated using high resolution transmission electron microscopy. We developed an automated process to directly generate computer models from parts of the measured microstructural data with great detail. Computer simulations of these finite element models provided information on the quality of the magnetic sample. The simulation of the properties and performance of the magnets requires a large amount of computing resources. We implemented and trained a machine learning model capable of predicting results in seconds, rather than weeks as required for the simulations. The model uses decision trees trained with the simulations results. Microstructural features, like a certain defect distribution, can be directly related to the quality of the magnet. This information is highly important for the magnet manufacturers. The generated computer models of the material are only small subsets of a magnet in a real size application. Hence, we developed an algorithm to combine the simulated and predicted results to obtain results for much bigger magnets. Such big models cannot be calculated otherwise. The algorithm reduces the complexity of the system, which is called a reduced order model, but still approximates the magnetic characteristics of the whole microstructure data very accurately. We analyzed large data sets of MnAl -C with the developed automated modeling procedure, the machine learning model, and the reduced order model. We learned that MnAl-C is a suitable material for permanent magnets, that are needed in environmental friendly applications. It is necessary to suppress the microstructural defects during production. If they cannot be completely removed, it is advisable to try reducing the grain size and the width of the defects.