Recent Advancements in Aluminum Alloy Research: Integrating Traditional Metallurgy with Machine Learning and Data-Driven Approaches
Source abstract
Abstract
Aluminum alloys are crucial for industries like aerospace, automotive, and electrical, among many others. Combining applied metallurgy with advancements in machine learning (ML) and data science is revolutionizing the alloy industry. This review highlights key breakthroughs, showing how ML can predict physical properties, optimize compositions, and simplify the integration of new alloys into manufacturing. Significant progress has been achieved in designing and discovering alloys using new computational methods, physics-informed neural networks, and active learning, with predictive accuracies over 92% and cost reductions exceeding 70% in alloy discovery. Challenges like data biases and model opacity still need to be addressed. Innovations in friction stir welding, additive manufacturing, and alloy recycling, paired with computational techniques, promise sustainable, high-performance alloys. The focus on high entropy alloys is just one example of new alloy development. This review emphasizes the growing role of ML in alloy design and the exciting potential for sustainable engineering.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Additive Manufacturing Materials and Processes · Aluminum Alloy Microstructure Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Department of Electrical Engineering, Razi University, Kermanshah 6714414971, Iran
Pooya Power Knowledge Enterprise, Tehran 1466993771, Iran
Department of Electrical Engineering, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain
Department of Physics, Faculty of Science, Lorestan University, Khorramabad 4431668151, Iran
Funding
ERDF · European Union · Agència de Gestió d’Ajuts Universitaris i de Recerca
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