[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118212-en":3,"doc-seo-118212-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118212,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning Framework for Predicting Reliability of Solder Joints - Dissertation","This dissertation presents techniques and a framework for building an explainable machine learning model to predict the reliability of solder joints subjected to thermal cycle testing. It addresses the limitation of black-box models whose complexity hides information needed to refine data, tune parameters, and support scientific discovery. The study explores machine learning methods with feature engineering, model selection, and parameter optimization, then applies explanation techniques using a generated dataset.","Portland State University  \nPDXScholar  \n\n| Dissertations and Theses | Dissertations and Theses |\n| --- | --- |\n| 8-30-2024\u003Cbr>Machine Learning Framework for Predicting Reliability of Solder Joints\u003Cbr>Robert Lee Jones\u003Cbr>Portland State University\u003Cbr>Follow this and additional works at: [https://pdxscholar.library.pdx.edu/open_access_etds](https://pdxscholar.library.pdx.edu/open_access_etds)\u003Cbr> Part of the Mechanical Engineering Commons\u003Cbr>Let us know how access to this document benefits you. |  |\n\nRecommended Citation  \nJones, Robert Lee, \"Machine Learning Framework for Predicting Reliability of Solder Joints\" (2024) . Dissertations and Theses. Paper 6695.  \nThis Dissertation is brought to you for free and open access. It has been accepted for inclusion in Dissertationsand Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [pdxscholar@pdx.edu](pdxscholar@pdx.edu).  \nMachine Learning Framework for Predicting Reliability of Solder Joints  \nby  \nRobert Lee Jones  \nA dissertation submitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nMechanical Engineering  \nDissertation Committee:  \nSung Yi, Chair  \nChien Wern  \nFaryar Etesami  \nRaj Solanki  \nPortland State University  \n2024  \n© 2024Robert Lee Jones  \ni  \nAbstract  \nPurpose: This paper presents the techniques and framework to produce an explainable machine learning model for predicting the reliability of solder joints exposed to thermal cycle testing. While machine learning techniques have become prevalent for many engineering and data analysis tasks, the complexity of the model occludes valuable information which can be used to refine the data, tune model parameters, and aid in scientific discovery. By exposing the relationships within the model, users can have greater confidence in the use and employment of it. The purpose of this study is to show how useful relationships can be obtained from a traditional ”black-box”machine learning model which can be utilized to investigate the model’s ability to extract and learn fundamental physical processes.  \nDesign/methodology/approach: Various machine learning techniques are explored with an emphasis on feature engineering, framework selection, and parameter optimization. Various model explanation methods are employed in conjunction with a generated dataset to highlight the relationships between the independent variable inputs and dependent variable output. The independent variable marginal contributions are derived from mean observations of surrogate model behavior, which can be compared to the mean observations of the machine learning model to determine relevant and exceptional behavior.  \nFindings: The application of machine learning techniques with experimental datasets certainly enables rapid evaluation of novel combinations in the problem space. While the end result is useful in itself, the ability to extract the influence of individual variable perturbations is quite challenging. The methods described herein provide this opportunity. The limitations of the model are determined by the quality and breadth of the data used to train the model parameters, in conjunction with model design specifications. While the accuracy of the determined relationships can be verified for some independent variables is possible, sparsely populated variables are less likely to  \nii  \ngenerate meaningful relationships that correlate to the expected behavior of physical phenomena. This creates an opportunity for researchers to determine which data is needed to improve the model behavior in accordance with known processes. Originality/value: The ability to predict thermal fatigue life accurately is extremely valuable to the industry because it saves time and cost for product development and optimization. This ability is improved when the model can be examined critically through the methods described herein.  \niii  \nDedication  \nTo my wife Lind","cbCaifus7rRF0Qxq","https://ap.wps.com/l/cbCaifus7rRF0Qxq","pdf",31302892,1,276,"English","en",105,"# Introduction\n## Objectives\n## Background and Motivation\n# Design / Methodology\n## Feature Engineering\n## Framework Selection and Parameter Optimization\n## Model Explanation Methods\n# Findings\n## Predictive Performance with Experimental Data\n## Variable Perturbation Influence\n## Data-Driven Limitations and Opportunities\n# Conclusion\n## Summary of Contributions","[{\"question\":\"What is the main goal of the dissertation?\",\"answer\":\"To develop an explainable machine learning framework that predicts solder joint reliability under thermal cycle testing and reveals relationships hidden in black-box models.\"},{\"question\":\"How does the study improve interpretability?\",\"answer\":\"It uses feature engineering, framework selection, parameter optimization, and model explanation methods with a generated dataset to expose relationships between inputs and outputs.\"},{\"question\":\"What factors limit the meaningfulness of the derived relationships?\",\"answer\":\"The quality and breadth of training data and model design specifications; sparsely populated variables are less likely to produce relationships consistent with expected physical behavior.\"}]","Machine Learning Framework for Predicting Reliability of Solder Joints - Dissertation | PDF",1785682248,696,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-framework-for-predicting-reliability-of-solder-joints-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-framework-for-predicting-reliability-of-solder-joints-dissertation/118212/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the dissertation?","Question",{"text":75,"@type":76},"To develop an explainable machine learning framework that predicts solder joint reliability under thermal cycle testing and reveals relationships hidden in black-box models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study improve interpretability?",{"text":80,"@type":76},"It uses feature engineering, framework selection, parameter optimization, and model explanation methods with a generated dataset to expose relationships between inputs and outputs.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors limit the meaningfulness of the derived relationships?",{"text":84,"@type":76},"The quality and breadth of training data and model design specifications; 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