Lidong Gu , Kongyuan Yang , Hongchang Ding , Zezhou Xu , Chunling Mao , Panpan Li , Zhenglei Yu , Yunting Guo , Luquan Ren
{"title":"机器学习在快速成型制造中的应用--镍钛合金的转化行为","authors":"Lidong Gu , Kongyuan Yang , Hongchang Ding , Zezhou Xu , Chunling Mao , Panpan Li , Zhenglei Yu , Yunting Guo , Luquan Ren","doi":"10.1016/j.matdes.2024.113443","DOIUrl":null,"url":null,"abstract":"<div><div>The laser powder bed fused NiTi alloys (LPBF-NiTi) demonstrate shape memory functionality and superelasticity as a result of their distinctive phase transition characteristics. Nevertheless, achieving precise control and regulation of the phase transition temperature poses a challenge, influenced by factors like powder composition and process parameter. In this study, a feature screening strategy and machine learning (ML) method were employed to predict and regulate the phase transition temperature of LPBF-NiTi alloy, offering a more efficient and cost-effective alternative to traditional experimental methods of regulation and control. Specifically, accuracy analysis was performed on LPBF-NiTi phase transition datasets with varying compositions and process conditions utilizing generalized regression neural network (GRNN), and other methods. The findings indicate that the interpretable features extracted through the selection strategy outlined in this study when combined with the GRNN model, demonstrate a high level of prediction accuracy (R<sup>2</sup> = 0.97). To investigate the accuracy of the model, this study utilized various process parameters to fabricate NiTi alloys with different compositions from Ni<sub>50.8</sub>Ti<sub>49.2</sub> alloy powder. Using this model, the study identified a novel, larger window of optimal LPBF processing that allows for controllable complex phase transitions.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"247 ","pages":"Article 113443"},"PeriodicalIF":7.6000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning in additive manufacturing——NiTi alloy’s transformation behavior\",\"authors\":\"Lidong Gu , Kongyuan Yang , Hongchang Ding , Zezhou Xu , Chunling Mao , Panpan Li , Zhenglei Yu , Yunting Guo , Luquan Ren\",\"doi\":\"10.1016/j.matdes.2024.113443\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The laser powder bed fused NiTi alloys (LPBF-NiTi) demonstrate shape memory functionality and superelasticity as a result of their distinctive phase transition characteristics. Nevertheless, achieving precise control and regulation of the phase transition temperature poses a challenge, influenced by factors like powder composition and process parameter. In this study, a feature screening strategy and machine learning (ML) method were employed to predict and regulate the phase transition temperature of LPBF-NiTi alloy, offering a more efficient and cost-effective alternative to traditional experimental methods of regulation and control. Specifically, accuracy analysis was performed on LPBF-NiTi phase transition datasets with varying compositions and process conditions utilizing generalized regression neural network (GRNN), and other methods. The findings indicate that the interpretable features extracted through the selection strategy outlined in this study when combined with the GRNN model, demonstrate a high level of prediction accuracy (R<sup>2</sup> = 0.97). To investigate the accuracy of the model, this study utilized various process parameters to fabricate NiTi alloys with different compositions from Ni<sub>50.8</sub>Ti<sub>49.2</sub> alloy powder. Using this model, the study identified a novel, larger window of optimal LPBF processing that allows for controllable complex phase transitions.</div></div>\",\"PeriodicalId\":383,\"journal\":{\"name\":\"Materials & Design\",\"volume\":\"247 \",\"pages\":\"Article 113443\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials & Design\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0264127524008189\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials & Design","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0264127524008189","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Machine learning in additive manufacturing——NiTi alloy’s transformation behavior
The laser powder bed fused NiTi alloys (LPBF-NiTi) demonstrate shape memory functionality and superelasticity as a result of their distinctive phase transition characteristics. Nevertheless, achieving precise control and regulation of the phase transition temperature poses a challenge, influenced by factors like powder composition and process parameter. In this study, a feature screening strategy and machine learning (ML) method were employed to predict and regulate the phase transition temperature of LPBF-NiTi alloy, offering a more efficient and cost-effective alternative to traditional experimental methods of regulation and control. Specifically, accuracy analysis was performed on LPBF-NiTi phase transition datasets with varying compositions and process conditions utilizing generalized regression neural network (GRNN), and other methods. The findings indicate that the interpretable features extracted through the selection strategy outlined in this study when combined with the GRNN model, demonstrate a high level of prediction accuracy (R2 = 0.97). To investigate the accuracy of the model, this study utilized various process parameters to fabricate NiTi alloys with different compositions from Ni50.8Ti49.2 alloy powder. Using this model, the study identified a novel, larger window of optimal LPBF processing that allows for controllable complex phase transitions.
期刊介绍:
Materials and Design is a multi-disciplinary journal that publishes original research reports, review articles, and express communications. The journal focuses on studying the structure and properties of inorganic and organic materials, advancements in synthesis, processing, characterization, and testing, the design of materials and engineering systems, and their applications in technology. It aims to bring together various aspects of materials science, engineering, physics, and chemistry.
The journal explores themes ranging from materials to design and aims to reveal the connections between natural and artificial materials, as well as experiment and modeling. Manuscripts submitted to Materials and Design should contain elements of discovery and surprise, as they often contribute new insights into the architecture and function of matter.