Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L
In aerospace engineering, many additive manufacturing (AM) metal parts subject to fatigue loadings, resulting in their fatigue failure. Therefore, it is essential to develop an advanced approach for fatigue issues. Although some theoretical methods are used for fatigue analysis of AM metal parts, th...
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| Main Authors: | , |
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| 格式: | Article |
| 語言: | English |
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2022
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| 在線閱讀: | https://hdl.handle.net/10356/154794 |
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