Study Results from Hohai University Provide New Insights into Machine Learning ( Machine learning in prediction of residual stress in laser shock peening for max imizing residual compressive stress formation)

河海大学的研究结果为机器学习(机器学习在激光冲击喷丸残余应力预测中的应用提供了新的见解,以最大限度地减少残余压应力形成)

Robotics & Machine Learning Daily News2024，Issue(Jul.2) ：55-55.

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Study Results from Hohai University Provide New Insights into Machine Learning ( Machine learning in prediction of residual stress in laser shock peening for max imizing residual compressive stress formation)

河海大学的研究结果为机器学习(机器学习在激光冲击喷丸残余应力预测中的应用提供了新的见解,以最大限度地减少残余压应力形成)

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摘要

由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-人工智能的新数据在一份新的报告中呈现。根据NewsRx记者来自中华人民共和国南京的新闻报道，研究表明，"Las ER冲击喷丸(LSP)是一种提高表面性能的先进技术，因其在材料中诱导有益残余应力的能力而引起了人们的极大兴趣。"国家自然科学基金资助本研究。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Nanj ing, People’s Republic of China, by NewsRx correspondents, research stated, “Las er Shock Peening (LSP) is an advanced technique for enhancing surface properties , drawing significant interest for its ability to induce beneficial residual str esses in materials.” Financial supporters for this research include National Natural Science Foundati on of China.

Key words

Hohai University/Nanjing/People's Repu blic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning

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出版年

2024

Robotics & Machine Learning Daily News

ISSN：

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