Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

• Published in: Integrating materials and manufacturing innovation Vol. 7; no. 3; pp. 157 - 172
• Main Authors: Dimiduk, Dennis M., Holm, Elizabeth A., Niezgoda, Stephen R.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2018; Springer Nature B.V; Springer Science + Business Media
• Subjects: Artificial intelligence; Characterization and Evaluation of Materials; Chemistry and Materials Science; Deep learning; Engineering; Engineering education; Machine learning; Machining; Masking; Materials information; Materials Science; Metallic Materials; Nanotechnology; New technology; Review Article; Structural Materials; Surfaces and Interfaces; Teaching methods; Thin Films; ICME; Deep learning; Digital engineering; Machine learning; Artificial intelligence; MGI; Multiscale modeling
• ISSN: 2193-9764; 2193-9772
• DOI: 10.1007/s40192-018-0117-8

The fields of machining learning and artificial intelligence are rapidly expanding, impacting nearly every technological aspect of society. Many thousands of published manuscripts report advances over the last 5 years or less. Yet materials and structures engineering practitioners are slow to engage with these advancements. Perhaps the recent advances that are driving other technical fields are not sufficiently distinguished from long-known informatics methods for materials, thereby masking their likely impact to the materials, processes, and structures engineering (MPSE). Alternatively, the diverse nature and limited availability of relevant materials data pose obstacles to machine-learning implementation. The glimpse captured in this overview is intended to draw focus to selected distinguishing advances, and to show that there are opportunities for these new technologies to have transformational impacts on MPSE. Further, there are opportunities for the MPSE fields to contribute understanding to the emerging machine-learning tools from a physics basis. We suggest that there is an immediate need to expand the use of these new tools throughout MPSE, and to begin the transformation of engineering education that is necessary for ongoing adoption of the methods.