{"title":"利用机器学习预测四元形状记忆合金 NiTiHfX 中的致动应变","authors":"","doi":"10.1016/j.commatsci.2024.113345","DOIUrl":null,"url":null,"abstract":"<div><p>Data-driven techniques are used to predict the actuation strain (AS) of NiTiHfX shape memory alloy (SMA). A Machine Learning (ML) approach is used to overcome the high dimensional dependency of NiTiHfX AS on numerous factors, as well as the lack of fully known governing physics. Detailed data extraction on available experimental studies is performed to gather any related information about the actuation strain. The elemental composition, manufacturing approaches, thermal treatments, applied stress, and post-processing steps that are commonly used to process NiTiHfX and have an impact on the material AS are used as input parameters of the ML models. Since a broad data collection is performed the information for each input factor was sufficient for the use of the majority of the accessible information in the literature on NiTiHfX AS. Considering most of the regular NiTiHfX processing factors also enables the option of tuning additional characteristics of NiTiHfX in addition to the ASs. The work is unique as is the first to fully investigate the NiTiHfX actuation strain prediction.</p><p>To forecast the NiTiHfX AS, a total of 901 data sets or 17,119 data points for eighteen inputs and one output were gathered, verified, and selected. Several machine-learning approaches were applied and joined to gather to guarantee robust modeling. The global model’s overall determination factor (R<sup>2</sup>) was 0.96, suggesting the viability of the proposed NN model. Such a model opens the possibility of intelligent material selection and processing to maximize the AS or shape memory effect of NiTiHf SMA.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0927025624005664/pdfft?md5=232342533694fd5540ee0b92d02bc792&pid=1-s2.0-S0927025624005664-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Predicting actuation strain in quaternary shape memory alloy NiTiHfX using machine learning\",\"authors\":\"\",\"doi\":\"10.1016/j.commatsci.2024.113345\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Data-driven techniques are used to predict the actuation strain (AS) of NiTiHfX shape memory alloy (SMA). A Machine Learning (ML) approach is used to overcome the high dimensional dependency of NiTiHfX AS on numerous factors, as well as the lack of fully known governing physics. Detailed data extraction on available experimental studies is performed to gather any related information about the actuation strain. The elemental composition, manufacturing approaches, thermal treatments, applied stress, and post-processing steps that are commonly used to process NiTiHfX and have an impact on the material AS are used as input parameters of the ML models. Since a broad data collection is performed the information for each input factor was sufficient for the use of the majority of the accessible information in the literature on NiTiHfX AS. Considering most of the regular NiTiHfX processing factors also enables the option of tuning additional characteristics of NiTiHfX in addition to the ASs. The work is unique as is the first to fully investigate the NiTiHfX actuation strain prediction.</p><p>To forecast the NiTiHfX AS, a total of 901 data sets or 17,119 data points for eighteen inputs and one output were gathered, verified, and selected. Several machine-learning approaches were applied and joined to gather to guarantee robust modeling. The global model’s overall determination factor (R<sup>2</sup>) was 0.96, suggesting the viability of the proposed NN model. Such a model opens the possibility of intelligent material selection and processing to maximize the AS or shape memory effect of NiTiHf SMA.</p></div>\",\"PeriodicalId\":10650,\"journal\":{\"name\":\"Computational Materials Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0927025624005664/pdfft?md5=232342533694fd5540ee0b92d02bc792&pid=1-s2.0-S0927025624005664-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Materials Science\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0927025624005664\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Materials Science","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0927025624005664","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
数据驱动技术用于预测 NiTiHfX 形状记忆合金 (SMA) 的致动应变 (AS)。机器学习(ML)方法用于克服 NiTiHfX AS 与众多因素的高维度依赖性,以及缺乏完全已知的控制物理学。对现有实验研究进行了详细的数据提取,以收集有关致动应变的任何相关信息。常用于加工 NiTiHfX 并对材料 AS 有影响的元素组成、制造方法、热处理、外加应力和后处理步骤被用作 ML 模型的输入参数。由于进行了广泛的数据收集,每个输入因素的信息都足以使用有关 NiTiHfX AS 的大部分文献信息。考虑到大多数常规的 NiTiHfX 加工因素,除了 AS 之外,还可以选择调整 NiTiHfX 的其他特性。为了预测 NiTiHfX AS,我们收集、验证并选择了 18 个输入和 1 个输出的 901 个数据集或 17119 个数据点。为保证建模的稳健性,我们采用了多种机器学习方法,并将其结合起来。全局模型的总体决定系数(R2)为 0.96,表明所提议的 NN 模型是可行的。这种模型为智能材料选择和加工提供了可能性,以最大限度地提高镍钛铪 SMA 的 AS 或形状记忆效应。
Predicting actuation strain in quaternary shape memory alloy NiTiHfX using machine learning
Data-driven techniques are used to predict the actuation strain (AS) of NiTiHfX shape memory alloy (SMA). A Machine Learning (ML) approach is used to overcome the high dimensional dependency of NiTiHfX AS on numerous factors, as well as the lack of fully known governing physics. Detailed data extraction on available experimental studies is performed to gather any related information about the actuation strain. The elemental composition, manufacturing approaches, thermal treatments, applied stress, and post-processing steps that are commonly used to process NiTiHfX and have an impact on the material AS are used as input parameters of the ML models. Since a broad data collection is performed the information for each input factor was sufficient for the use of the majority of the accessible information in the literature on NiTiHfX AS. Considering most of the regular NiTiHfX processing factors also enables the option of tuning additional characteristics of NiTiHfX in addition to the ASs. The work is unique as is the first to fully investigate the NiTiHfX actuation strain prediction.
To forecast the NiTiHfX AS, a total of 901 data sets or 17,119 data points for eighteen inputs and one output were gathered, verified, and selected. Several machine-learning approaches were applied and joined to gather to guarantee robust modeling. The global model’s overall determination factor (R2) was 0.96, suggesting the viability of the proposed NN model. Such a model opens the possibility of intelligent material selection and processing to maximize the AS or shape memory effect of NiTiHf SMA.
期刊介绍:
The goal of Computational Materials Science is to report on results that provide new or unique insights into, or significantly expand our understanding of, the properties of materials or phenomena associated with their design, synthesis, processing, characterization, and utilization. To be relevant to the journal, the results should be applied or applicable to specific material systems that are discussed within the submission.