[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120728-en":3,"doc-seo-120728-105":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":20,"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},120728,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Comparing the Performance of Different Machine Learning Models in the Evaluation of Solder Joint Fatigue Life Under Thermal Cycling","Predicting the reliability of board-level solder joints is complex because solder fatigue life depends on many design parameters and nonlinear, coupled phenomena. This thesis evaluates multiple machine learning models for predicting solder joint fatigue life using experimental data from diverse thermal fatigue tests. Regression is conducted with artificial neural networks, random forests, gradient boosting, XGBoost, and AdaBoost with neural networks. A web-based Electronics Packaging Materials Database (EPMD) is developed to curate, store, share, and serve the training data. Results indicate regression-tree approaches can also be effective. The study emphasizes the need for large curated datasets.","Portland State University  \nPDXScholar  \n\n| Dissertations and Theses | Dissertations and Theses |\n| --- | --- |\n| 1-18-2023\u003Cbr>Comparing the Performance of Different Machine Learning Models in the Evaluation of Solder Joint Fatigue Life Under Thermal Cycling\u003Cbr>Jason Scott Ross\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  \nRoss, Jason Scott, \"Comparing the Performance of Different Machine Learning Models in the Evaluation of Solder Joint Fatigue Life Under Thermal Cycling\" (2023) . Dissertations and Theses. Paper 6358.  \nThis Thesis is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [pdxscholar@pdx.edu](pdxscholar@pdx.edu).  \nComparing the Performance of Different Machine Learning Models in the Evaluation of Solder Joint Fatigue Life Under Thermal Cycling  \nby  \nJason Scott Ross  \nA thesis submitted in partial fulfillment of the requirements for the degree of  \nMaster of Science  \nin  \nMechanical Engineering  \nThesis Committee:  \nSung Yi, Chair  \nHormoz Zareh  \nChien Wern  \nPortland State University  \n2023  \n© 2022 Jason Scott Ross  \nAbstract  \nPredicting the reliability of board-level solder joints is a challenging process for the designer because the fatigue life of solder is influenced by a large variety of design parameters and many nonlinear, coupled phenomena. Machine learning has shown promise as a way of predicting the fatigue life of board-level solder joints. In the present work, the performance of various machine learning models to predict the fatigue life of board-level solder joints is discussed. Experimental data from many different solder joint thermal fatigue tests are used to train the different machine learning models. A web-based database for storing, sharing, and uploading data related to the performance of electronics materials, the Electronics Packaging Materials Database (EPMD), has been developed and used to store and serve the training data for the present work. Data regression is performed using artificial neural networks, random forests, gradient boosting, extreme gradient boosting (XGBoost), and adaptive boosting with neural networks (AdaBoost) . While previous works have studied artificial neural networks as a way to predict the fatigue life of board-level solder joints, the results in this paper suggest that machine learning techniques based on regression trees may also be useful in predicting the fatigue life of board-level solder joints. This paper also demonstrates the need for a large collection of curated data related to board-level solder joint reliability, and presents the Electronics Packaging  \nMaterials Database to meet that need.  \nDedication  \nI dedicate this to Kaitlyn. Thank you for your love, support, kindness, patience,  \nand understanding.  \nTable of Contents  \nAbstract i  \nDedication ii  \nList of Tables v  \nList of Figures vi  \nGlossary viii  \n1. Introduction 1  \n2. Failure mechanisms of board-level solder joints 8  \n3. Data Sources 12  \n4. Feature Selection 14  \n5. Regression 17  \n5.1. Data Retrieval ............................... 18  \n5.2. Data Pre-processing ........................... 18  \n5.2.1. Missing parameter imputation .................. 19  \n5.2.2. Data point bootstrapping .................... 19  \n5.2.3. Feature Selection ......................... 20  \n5.2.4. Missing Value Rejection ..................... 20  \n5.2.5. Small Category Rejection .................... 21  \n5.2.6. Encoding categorical features .................. 21  \n5.2.7. Scaling numerical features .................... 22  \n5.3. Model Evaluation ............................. 22  \n5.4. ","cbCaikn5xrQJPGMR","https://ap.wps.com/l/cbCaikn5xrQJPGMR","pdf",2537155,1,117,"English","en",105,"# Abstract\n# Dedication\n# List of Tables\n# List of Figures\n# Glossary\n# Introduction\n# Failure mechanisms of board-level solder joints\n# Data Sources\n# Feature Selection\n# Regression\n## Data Retrieval\n## Data Pre-processing\n## Model Evaluation\n## Models\n# Results\n# Conclusions\n# References\n# Appendix A. Computing parameters of the Weibull distribution\n# Appendix B. Database architecture\n# Appendix C. Plot Digitization\n# Appendix D. Data Values\n# Appendix E. Nested K-Fold Cross-Validation\n# Appendix F. Backpropagation","[{\"question\":\"Why is predicting board-level solder joint reliability challenging?\",\"answer\":\"Fatigue life depends on many design parameters and involves nonlinear, coupled phenomena, making prediction difficult for designers.\"},{\"question\":\"Which machine learning models are used to predict solder joint fatigue life?\",\"answer\":\"The work uses artificial neural networks, random forests, gradient boosting, XGBoost, and AdaBoost with neural networks, applying regression to the training data.\"},{\"question\":\"How is training data managed for model development and evaluation?\",\"answer\":\"Experimental data from many thermal fatigue tests are curated and stored in a web-based Electronics Packaging Materials Database (EPMD), which supports uploading and serving training data.\"}]","Comparing the Performance of Different Machine Learning Models in the Evaluation of Solder Joint Fatigue Life Under Thermal Cycling | 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