A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset
文献类型：期刊论文
| 作者 | Hu, Bing-Li1,2; Luo, Yan-Wen3; Zhang, Bin3; Zhang, Guang-Ping2 |
| 刊名 | MATERIALS
 |
| 出版日期 | 2023-10-01 |
| 卷号 | 16期号:19页码:23 |
| 关键词 | small dataset machine learning algorithm fatigue life additive manufacturing |
| DOI | 10.3390/ma16196606 |
| 通讯作者 | Zhang, Guang-Ping(gpzhang@imr.ac.cn) |
| 英文摘要 | Fatigue life prediction of Inconel 718 fabricated by laser powder bed fusion was investigated using a miniature specimen tests method and machine learning algorithms. A small dataset-based machine learning framework integrating thirteen kinds of algorithms was constructed to predict the pore-influenced fatigue life. The method of selecting random seeds was employed to evaluate the performance of the algorithms, and then the ranking of various machine learning algorithms for predicting pore-influenced fatigue life on small datasets was obtained by verifying the prediction model twenty or thirty times. The results showed that among the thirteen popular machine learning algorithms investigated, the adaptive boosting algorithm from the boosting category exhibited the best fitting accuracy for fatigue life prediction of the additively manufactured Inconel 718 using the small dataset, followed by the decision tree algorithm in the nonlinear category. The investigation also found that DT, RF, GBDT, and XGBOOST algorithms could effectively predict the fatigue life of the additively manufactured Inconel 718 within the range of 1 x 105 cycles on a small dataset compared to others. These results not only demonstrate the capability of using small dataset-based machine learning techniques to predict fatigue life but also may guide the selection of algorithms that minimize performance evaluation costs when predicting fatigue life. |
| 资助项目 | National Key R&D Program of China[2022YFB4601000] ; National Natural Science Foundation of China (NSFC)[52171128] ; National Natural Science Foundation of China (NSFC)[51971060] ; Fundamental Research Project of Shenyang National Laboratory for Materials Science[L2019R18] |
| WOS研究方向 | Chemistry ; Materials Science ; Metallurgy & Metallurgical Engineering ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001083341400001 |
| 出版者 | MDPI |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China (NSFC) ; Fundamental Research Project of Shenyang National Laboratory for Materials Science |
| 源URL | [http://ir.imr.ac.cn/handle/321006/179494]  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Zhang, Guang-Ping |
| 作者单位 | 1.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, 72 Wenhua Rd, Shenyang 110016, Peoples R China 3.Northeastern Univ, Sch Mat Sci & Engn, Minist Educ, Key Lab Anisotropy & Texture Mat, 3-11 Wenhua Rd, Shenyang 110819, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Bing-Li,Luo, Yan-Wen,Zhang, Bin,et al. A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset[J]. MATERIALS,2023,16(19):23. |
| APA | Hu, Bing-Li,Luo, Yan-Wen,Zhang, Bin,&Zhang, Guang-Ping.(2023).A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset.MATERIALS,16(19),23. |
| MLA | Hu, Bing-Li,et al."A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset".MATERIALS 16.19(2023):23. |
入库方式： OAI收割
来源：金属研究所
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