随机振动环境下焊点疲劳寿命模型的全局不确定性分析

Ching-Yuan Kao, Mei-Ling Wu
{"title":"随机振动环境下焊点疲劳寿命模型的全局不确定性分析","authors":"Ching-Yuan Kao, Mei-Ling Wu","doi":"10.1109/IMPACT.2011.6117285","DOIUrl":null,"url":null,"abstract":"The global model uncertainty analysis is to estimate the error due to input data approximations rather than sensitivities associated with finite element modeling. In this paper, estimates will be made on the amount of sensitivity in the global modeling which basically involves the sensitivity in the value of the material properties, input loadings, and geometries. Assume the model structure is corrected, how do we estimate a physics of failure (PoF) and predict time to failure or cycle to failure? Identifying an approach for quantifying the combination of input and uncertainty would enable the determination of more realistic confidence limits on Physics of Failure (PoF) predictions. The physics-of-failure (PoF) approach to reliability utilizes knowledge of the life-cycle load (thermal, vibration, mechanical, electrical, photonics, and so on) profile package architecture and material properties to identify potential failure mechanisms and to prevent operational failures through robust design and manufacturing practices. We can decompose the elements of any mathematical model into the physical parameters used in forming equations, such as material properties or geometric dimensions and the model structure, which results from simplifications, assumptions and approximations. Variability in the physical parameters can be addressed by existing stochastic methods, which are designed to propagate probability distributions on the parameters through a fixed model structure in order to estimate the statistics of interest on the model response quantities.","PeriodicalId":6360,"journal":{"name":"2011 6th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)","volume":"38 1","pages":"482-484"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Global uncertainty analysis of solder joint fatigue life model in random vibration environment\",\"authors\":\"Ching-Yuan Kao, Mei-Ling Wu\",\"doi\":\"10.1109/IMPACT.2011.6117285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The global model uncertainty analysis is to estimate the error due to input data approximations rather than sensitivities associated with finite element modeling. In this paper, estimates will be made on the amount of sensitivity in the global modeling which basically involves the sensitivity in the value of the material properties, input loadings, and geometries. Assume the model structure is corrected, how do we estimate a physics of failure (PoF) and predict time to failure or cycle to failure? Identifying an approach for quantifying the combination of input and uncertainty would enable the determination of more realistic confidence limits on Physics of Failure (PoF) predictions. The physics-of-failure (PoF) approach to reliability utilizes knowledge of the life-cycle load (thermal, vibration, mechanical, electrical, photonics, and so on) profile package architecture and material properties to identify potential failure mechanisms and to prevent operational failures through robust design and manufacturing practices. We can decompose the elements of any mathematical model into the physical parameters used in forming equations, such as material properties or geometric dimensions and the model structure, which results from simplifications, assumptions and approximations. Variability in the physical parameters can be addressed by existing stochastic methods, which are designed to propagate probability distributions on the parameters through a fixed model structure in order to estimate the statistics of interest on the model response quantities.\",\"PeriodicalId\":6360,\"journal\":{\"name\":\"2011 6th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)\",\"volume\":\"38 1\",\"pages\":\"482-484\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 6th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMPACT.2011.6117285\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 6th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMPACT.2011.6117285","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

全局模型不确定性分析是估计由于输入数据近似引起的误差,而不是与有限元建模相关的灵敏度。在本文中,将对全局建模中的敏感性进行估计,这主要涉及材料属性值、输入载荷和几何形状的敏感性。假设模型结构是正确的,我们如何估计物理故障(PoF)并预测故障时间或故障周期?确定一种量化输入和不确定性组合的方法将能够确定故障物理(PoF)预测的更现实的置信限制。可靠性的物理失效(PoF)方法利用生命周期负载(热、振动、机械、电气、光子学等)、封装结构和材料特性的知识来识别潜在的失效机制,并通过稳健的设计和制造实践来防止操作故障。我们可以将任何数学模型的元素分解为用于形成方程的物理参数,例如材料性质或几何尺寸以及模型结构,这些参数是通过简化,假设和近似得到的。物理参数的可变性可以通过现有的随机方法来解决,这些方法旨在通过固定的模型结构传播参数的概率分布,以估计模型响应量的相关统计量。
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
Global uncertainty analysis of solder joint fatigue life model in random vibration environment
The global model uncertainty analysis is to estimate the error due to input data approximations rather than sensitivities associated with finite element modeling. In this paper, estimates will be made on the amount of sensitivity in the global modeling which basically involves the sensitivity in the value of the material properties, input loadings, and geometries. Assume the model structure is corrected, how do we estimate a physics of failure (PoF) and predict time to failure or cycle to failure? Identifying an approach for quantifying the combination of input and uncertainty would enable the determination of more realistic confidence limits on Physics of Failure (PoF) predictions. The physics-of-failure (PoF) approach to reliability utilizes knowledge of the life-cycle load (thermal, vibration, mechanical, electrical, photonics, and so on) profile package architecture and material properties to identify potential failure mechanisms and to prevent operational failures through robust design and manufacturing practices. We can decompose the elements of any mathematical model into the physical parameters used in forming equations, such as material properties or geometric dimensions and the model structure, which results from simplifications, assumptions and approximations. Variability in the physical parameters can be addressed by existing stochastic methods, which are designed to propagate probability distributions on the parameters through a fixed model structure in order to estimate the statistics of interest on the model response quantities.
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
自引率
0.00%
发文量
0
期刊最新文献
Comparison the reliability of small plated-through hole with different diameters under thermal stress Co-simulation of capacitive coupling pads assignment for capacitive coupling interconnection applications Microstructure evolution in a sandwich structure of Ni/SnAg/Ni microbump during reflow Comparison among individual thermal cycling, vibration test and the combined test for the life estimation of electronic components Limitations of gluing as a replacement of ultrasonic welding: Attaching Lithium battery contacts to PCBs
×
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