Quantitative Benchmarking of Acoustic Emission Machine Learning Frameworks for Damage Mechanism Identification

IF 2.4 3区材料科学 Q3 ENGINEERING, MANUFACTURING

Integrating Materials and Manufacturing Innovation Pub Date : 2023-02-13 DOI:10.1007/s40192-023-00293-8

C. Muir, N. Tulshibagwale, A. Furst, Balakumar Swaminathan, A. Almansour, K. Sevener, M. Presby, J. D. Kiser, T. Pollock, S. Daly, C. Smith

引用次数: 1

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损伤机制识别声发射机器学习框架的定量基准测试

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来源期刊

Integrating Materials and Manufacturing Innovation Engineering-Industrial and Manufacturing Engineering

CiteScore

5.30

自引率

9.10%

发文量

审稿时长

39 days

期刊介绍： The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.