More than three decades ago, at Fraunhofer IZFP, research activities that were related to the application of micromagnetic methods for nondestructive testing (NDT) of the microstructure and the properties of ferrous materials commenced. Soon, it was observed that it is beneficial to combine the measuring information from several micromagnetic methods and measuring parameters. This was the birth of 3MA-the micromagnetic multi-parametric microstructure and stress analysis. Since then, 3MA has undergone a remarkable development. It has proven to be one of the most valuable testing techniques for the nondestructive characterization of metallic materials. Nowadays, 3MA is well accepted in industrial production and material research. Over the years, several equipment variants and a wide range of probe heads have been developed, ranging from magnetic microscopes with µm resolution up to large inspection systems for in-line strip steel inspection. 3MA is extremely versatile, as proved by a huge amount of reported applications, such as the quantitative determination of hardness, hardening depth, residual stress, and other material parameters. Today, specialized 3MA systems are available for manual or automated testing of various materials, semi-finished goods, and final products that are made of steel, cast iron, or other ferromagnetic materials. This paper will provide an overview of the historical development, the basic principles, and the main applications of 3MA.

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Friction stir welding as a solid-state joining method with its comparatively low process temperatures is suitable for joining dissimilar materials like aluminum/magnesium or aluminum/steel. Such hybrid joints are of great interest regarding lightweight efforts in different industrial fields like the transportation area. The present work investigates the influence of additionally transmitted power ultrasound during the friction stir welding on the joint properties of EN AC-48000/AZ91 and EN AW-6061/DP600. Therefore, conventional friction stir welding was continuously compared to ultrasound enhanced friction stir welding. Light microscopic analysis and nondestructive testing of the joints using x-ray and high frequency ultrasound show different morphologies of the nugget for the aluminum/magnesium joints as well as differences in the amount and size of steel particles in the nugget of aluminum/steel joints. Scanning electron microcopy proves differences in the thickness of continuous intermetallic layers for the aluminum/steel joints realized with and without power ultrasound. Regarding the tensile strength of the joints the power ultrasound leads to increased joint strengths for EN AC-48000/AZ91 joints compared to a decrease for EN AW-6061/DP600 joints. Corrosion investigations show an influence of the ultrasound power on the corrosion properties of EN AC-48000/AZ91 joints which is attributed to a changed aluminum content in the nugget region. Because of the great potential difference between the magnesium and the nugget phase the transitional area exhibits strong galvanic corrosion. For EN AW-6061/DP600 joints an increased corrosion caused by galvanic effects is not expected as the potentials of the EN AW-6061 aluminum alloy and DP600 steel are very similar.

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AbstractMicromagnetic materials characterization is a nondestructive means of predicting mechanical properties and stress of steel and iron products. The method is based on the circumstance that both mechanical and magnetic behaviour relate to microstructure over similar interaction mechanisms, which leads to characteristic correlations between mechanical and magnetic properties of ferromagnetic materials. The prediction of mechanical properties or stress from micromagnetic parameters represents an inverse problem commonly addressed by regression and classification approaches. Challenges for the industrial application of micromagnetic methods lie in the development of robust sensors, definition of significant features, and implementation of powerful machine learning algorithms for a reliable quantitative target value prediction by processing of the micromagnetic features. This contribution briefly explains the background of micromagnetics, describes the typical challenges experienced in practice and provides insight into latest progress in the application of machine learning to micromagnetic data.