Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
Open accessAbstract
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.
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The implementation of AI and ML applications in advanced robotics can significantly reduce costs associated with labor and maintenance.
“Cost Reduction: The implementation of AI and ML applications in advanced robotics can significantly reduce costs associated with labor and maintenance [54] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [54]
The Introduction and Motivation describes ML/AI in autonomous machines, including robots, but does not discuss labor or maintenance costs. Materials and Processes Discovery discusses the expense of experiments and computation, not cost reductions from advanced robotics. Searches for labor costs, maintenance, and cost reduction found no passage supporting the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAI has shown immense promise in transforming healthcare through the application of advanced technologies like machine learning and deep learning.
“Artificial intelligence (AI) has shown immense promise in transforming healthcare through the application of advanced technologies like machine learning (ML) and deep learning (DL) [1], [2].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [2]
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References 172 · Cited by 347
Research works printed but not linked: 12
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- Authors
- Dimiduk, D. M., Holm, E. A., & Niezgoda, S. R.
- Affiliations
- The Ohio State University (US)
- Carnegie Mellon University (US)
- Corresponding author
- Dimiduk, Dennis M.
- Venue
- Integrating Materials and Manufacturing Innovation
- Published
- 2018-08-27
- Volume
- 7
- Issue
- 3
- Pages
- 157-172
- ISSN
- 2193-9764 (ISSN-L)
- 2193-9772
- OpenAlex
- W2889393096
- Cited by
- 347
- References
- 172
- Citations by year
- 2026: 27 · 2025: 49 · 2024: 55 · 2023: 60 · 2022: 48 · 2021: 51 · 2020: 37 · 2019: 18 · 2018: 2
- FWCI
- 10.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Integrating materials and manufacturing innovation (publishedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Glenn Research Center (NNC15BA06B)
- College of Engineering, Ohio State University
- Defense Advanced Research Projects Agency (D15AP00103)
- U.S. Department of Energy (FE0027776)
- National Science Foundation (DMR-1507830)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- machine learning, deep learning, artificial intelligence, materials engineering, production process, structural engineering, materials data, physics-informed machine learning, engineering education, technology adoption
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