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This is a Literature Review study.

October 10, 2025

Review of State-of-the-Art Machine Learning Research for Structural Integrity Assessment of Welded Joints

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Authors

SESamuel EkaUniversity of Manchester NPNorman W. PatonUniversity of Manchester ASA.H. SherryUniversity of Manchester

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Implication

Review highlights machine learning's role in enhancing structural integrity assessments of welded joints, suggesting improvements in operational safety.

Key Points

Machine learning improves structural integrity assessments, reducing risks in critical plant operations.
Key areas include weld quality classification and optimization, highlighting machine learning's versatile applications.
The paper outlines machine learning model development stages, emphasizing the importance of data collection and evaluation metrics.
Constructing effective machine learning models requires diverse datasets for better predictions and safety outcomes.

Cite This Study

Eka et al. (2025) studied this question.

synapsesocial.com/papers/68e8619c7ef2f04ca37e4389 https://doi.org/10.1115/pvp2025-155746

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Implication

Review highlights machine learning's role in enhancing structural integrity assessments of welded joints, suggesting improvements in operational safety.

Review of State-of-the-Art Machine Learning Research for Structural Integrity Assessment of Welded Joints

Discussion

Member takes

Implication

Key Points

Cite This Study

Review of State-of-the-Art Machine Learning Research for Structural Integrity Assessment of Welded Joints

Discussion

Member takes

Implication

Key Points

Cite This Study

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