Abstract
Materials science research begins in laboratories with testing the properties of metals and their alloys, the properties of the material depending on the type of additives and microstructure, as well as the changes in these properties taking place under the influence of processing. The next step is modeling and simulation of processes to investigate the possibility of their control and monitoring under production conditions. Some studies relate to an ongoing process, and then the research focuses on quality control of the process, optimization, and detection of irregularities and product defects. At all stages of research, it is possible to apply the methods of machine learning to the extent chosen by the analyst or expert. These methods can be used to obtain knowledge about occurring phenomena, research planning, and designing of production processes (in accordance with the 4th paradigm of science), but they can also be data-driven models given the possibility of autonomous control of a selected aspect of production (in accordance with the idea of the 4th industrial revolution). This paper presents an overview of ML methods based on examples taken from the field of materials science discussed in terms of materials–processes–knowledge formalization.
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This study was carried out as part of the fundamental research financed by the Ministry of Science and Higher Education, grant no. 16.16.110.663.
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Mrzygłód, B., Regulski, K., Opaliński, A. (2022). Machine Learning Studies in Materials Science. In: Datta, S., Davim, J.P. (eds) Machine Learning in Industry. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-75847-9_6
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