基于概率神经网络识别的镍基高温合金激光修复设计

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2022-10-10 DOI:10.1017/dce.2022.31
Freddie Markanday, G. Conduit, B. Conduit, J. Pürstl, K. Christofidou, L. Chechik, G. Baxter, C. Heason, H. Stone
{"title":"基于概率神经网络识别的镍基高温合金激光修复设计","authors":"Freddie Markanday, G. Conduit, B. Conduit, J. Pürstl, K. Christofidou, L. Chechik, G. Baxter, C. Heason, H. Stone","doi":"10.1017/dce.2022.31","DOIUrl":null,"url":null,"abstract":"Abstract A neural network framework is used to design a new Ni-based superalloy that surpasses the performance of IN718 for laser-blown-powder directed-energy-deposition repair applications. The framework utilized a large database comprising physical and thermodynamic properties for different alloy compositions to learn both composition to property and also property to property relationships. The alloy composition space was based on IN718, although, W was additionally included and the limiting Al and Co content were allowed to increase compared standard IN718, thereby allowing the alloy to approach the composition of ATI 718Plus® (718Plus). The composition with the highest probability of satisfying target properties including phase stability, solidification strain, and tensile strength was identified. The alloy was fabricated, and the properties were experimentally investigated. The testing confirms that this alloy offers advantages for additive repair applications over standard IN718.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Design of a Ni-based superalloy for laser repair applications using probabilistic neural network identification\",\"authors\":\"Freddie Markanday, G. Conduit, B. Conduit, J. Pürstl, K. Christofidou, L. Chechik, G. Baxter, C. Heason, H. Stone\",\"doi\":\"10.1017/dce.2022.31\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract A neural network framework is used to design a new Ni-based superalloy that surpasses the performance of IN718 for laser-blown-powder directed-energy-deposition repair applications. The framework utilized a large database comprising physical and thermodynamic properties for different alloy compositions to learn both composition to property and also property to property relationships. The alloy composition space was based on IN718, although, W was additionally included and the limiting Al and Co content were allowed to increase compared standard IN718, thereby allowing the alloy to approach the composition of ATI 718Plus® (718Plus). The composition with the highest probability of satisfying target properties including phase stability, solidification strain, and tensile strength was identified. The alloy was fabricated, and the properties were experimentally investigated. The testing confirms that this alloy offers advantages for additive repair applications over standard IN718.\",\"PeriodicalId\":34169,\"journal\":{\"name\":\"DataCentric Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2022-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"DataCentric Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/dce.2022.31\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"DataCentric Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/dce.2022.31","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 1

摘要

摘要使用神经网络框架设计了一种新型镍基高温合金,该合金的性能优于IN718,可用于激光吹制粉末定向能沉积修复应用。该框架利用包括不同合金成分的物理和热力学性质的大型数据库来学习成分与性质以及性质与性质的关系。合金成分空间以IN718为基础,但W被额外包括在内,并且与标准IN718相比,限制Al和Co含量被允许增加,从而使合金接近ATI 718Plus®(718Plus)的成分。确定了具有最高概率满足目标性能的成分,包括相稳定性、凝固应变和拉伸强度。制备了该合金,并对其性能进行了实验研究。测试证实,与标准IN718相比,该合金在添加剂修复应用方面具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Design of a Ni-based superalloy for laser repair applications using probabilistic neural network identification
Abstract A neural network framework is used to design a new Ni-based superalloy that surpasses the performance of IN718 for laser-blown-powder directed-energy-deposition repair applications. The framework utilized a large database comprising physical and thermodynamic properties for different alloy compositions to learn both composition to property and also property to property relationships. The alloy composition space was based on IN718, although, W was additionally included and the limiting Al and Co content were allowed to increase compared standard IN718, thereby allowing the alloy to approach the composition of ATI 718Plus® (718Plus). The composition with the highest probability of satisfying target properties including phase stability, solidification strain, and tensile strength was identified. The alloy was fabricated, and the properties were experimentally investigated. The testing confirms that this alloy offers advantages for additive repair applications over standard IN718.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
Semantic 3D city interfaces—Intelligent interactions on dynamic geospatial knowledge graphs Optical network physical layer parameter optimization for digital backpropagation using Gaussian processes Finite element model updating with quantified uncertainties using point cloud data Evaluating probabilistic forecasts for maritime engineering operations Bottom-up forecasting: Applications and limitations in load forecasting using smart-meter data
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1