Image-Based Machine Learning Methods in Materials Microstructure and Failure Analysis General Motors LLC

Format:

Book

Conference/Event

Author/Creator:

Akbari, Meysam, author.

Contributor:

Wang, Andy

Wang, Qigui

Yan, Cuifen

Conference Name:

WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Image-based machine learning (ML) methods are increasingly transforming the field of materials science, offering powerful tools for automatic analysis of microstructures and failure mechanisms. This paper provides an overview of the latest advancements in ML techniques applied to materials microstructure and failure analysis, with a particular focus on the automatic detection of porosity and oxide defects and microstructure features such as dendritic arms and eutectic phase in aluminum casting. By leveraging image-based data, such as metallographic and fractographic images, ML models can identify patterns that are difficult to detect through conventional methods. The integration of convolutional neural networks (CNNs) and advanced image processing algorithms not only accelerates the analysis process but also improves accuracy by reducing subjectivity in interpretation. Key studies and applications are further reviewed to highlight the benefits, challenges, and future directions of using ML in material failure analysis through image processing

Notes:

Vendor supplied data

Publisher Number:

2025-01-8324

Access Restriction:

Restricted for use by site license