التفاصيل البيبلوغرافية
| العنوان: |
Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions. |
| المؤلفون: |
Matin, Mahmood, Azadi, Mohammad |
| المصدر: |
Fracture & Structural Integrity / Frattura ed Integrità Strutturale; Apr2024, Issue 68, p357-370, 14p |
| مصطلحات موضوعية: |
MACHINE learning, ALLOY fatigue, HIGH cycle fatigue, ALUMINUM alloying, ALUMINUM alloy fatigue, FATIGUE limit, ALUMINUM alloys, CORROSION fatigue |
| مستخلص: |
This document is a compilation of various research papers and articles that discuss the use of machine learning and other techniques to predict fatigue lifetimes in different materials and structures. The papers cover topics such as the use of neural networks, XGBoost, and physics-informed machine learning models for fatigue prediction. The research focuses on different materials, including aluminum alloys and lead-free solders, and explores the effects of factors such as wear, lubrication, corrosion, and stress on fatigue life. The papers provide insights into the application of machine learning techniques in predicting fatigue lifetime and offer potential solutions for improving the accuracy of these predictions. [Extracted from the article] |
|
Copyright of Fracture & Structural Integrity / Frattura ed Integrità Strutturale is the property of Gruppo Italiano Frattura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder