[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120103-en":3,"doc-seo-120103-105":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},120103,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Simulation-Informed Optimization and Machine Learning for Advanced Manufacturing","Modern manufacturing requires optimizing chemical properties, material composition, and processing parameters to meet performance benchmarks under engineering and design constraints. Instead of relying on iterative trial-and-error or brute-force design of experiments, this dissertation leverages rapid multi-physics simulation and efficient machine learning/optimization methods to reduce time, cost, and environmental impact. It develops an integrated simulation-informed optimization framework combining experimentation, modeling/simulation, numerical optimization, and learning, demonstrated through acrylate-based UV-curable inks for additive manufacturing.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nSimulation-Informed Optimization and Machine Learning for Advanced Manufacturing  \nPermalink  \n[https://escholarship.org/uc/item/8p61g06t](https://escholarship.org/uc/item/8p61g06t)  \nAuthor  \nHowell, Brian M  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nSimulation-Informed Optimization and Machine Learning for Advanced Manufacturing  \nby  \nBrian Matthew Howell  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nEngineering Science-Mechanical Engineering  \nand the Designated Emphasis  \nin  \nComputational Data Science and Engineering  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nProfessor Tarek Zohdi, Chair  \nAssociate Professor Grace Gu  \nProfessor Jon Wilkening  \nSummer 2024  \nSIMULATION-INFORMED OPTIMIZATION AND MACHINE LEARNING FOR  \nADVANCED MANUFACTURING  \nCOPYRIGHT 2024  \nBY  \nBRIAN MATTHEW HOWELL  \nSimulation-Informed Optimization and Machine Learning for Advanced Manufacturing © 2024 by Brian M Howell is licensed under Creative Commons Attribution 4 .0 International. To view a copy of this license, visit [https://creativecommons. org/licenses/by/4.0/](https://creativecommons. org/licenses/by/4.0/)  \nAll Rights Reserved  \nNo part of this work may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law.  \n1  \nAbstract  \nSimulation-Informed Optimization and Machine Learning for Advanced Manufacturing  \nby  \nBrian Matthew Howell  \nDoctor of Philosophy in Engineering Science-Mechanical Engineering and the Designated Emphasis in  \nComputational Data Science and Engineering  \nUniversity of California, Berkeley  \nProfessor Tarek Zohdi, Chair  \nIn modern manufacturing, optimizing chemical properties, material composition, and processing parameters is essential for achieving desired performance benchmarks given manufacturing and design constraints. Traditional methods often rely on iterative trial-and-error or brute-force design of experiments (DOE), where materials and operating parameters are selected based on intuition and experience. This process is typically repeated until critical benchmarks are met or resources are depleted. Recent advancements in computing are beginning to transform this approach, enabling rapid multi-physics simulations and efficient machine learning/optimization algorithms that offer significant advantages over traditional DOE methods. These simulations are faster, more cost-effective, and environmentally friendly, reducing engineering time and manufacturing resources while minimizing overall development risk.  \nThis work presents an integrated approach that combines experimentation, multi-physics modeling/simulation, numerical optimization, and machine learning techniques. These components are integrated into a cohesive, simulation-informed optimization framework for designing materials in advanced manufacturing applications. This dissertation demonstrateshow these components interact and inform each other in the context of designing acrylatebased UV-curable inks for additive manufacturing processes. Specifically, it illustrates how multi-physics modeling provides a virtual environment, and how Evolutionary Strategies and Bayesian Optimization accelerate the search for optimal input parameters within experimentally determined constraints. This comprehensive approach not only offers a more efficient method for addressing formulation strategies in additive manufacturing but also paves the way for general material deve","cbCaihbS4ZrXPm5G","https://ap.wps.com/l/cbCaihbS4ZrXPm5G","pdf",32950208,1,205,"English","en",105,"# Introduction\n## Motivation\n## Simulation-Informed Optimization Framework\n## Outline of Work\n# Background and Mathematical Tools\n## Additive Manufacturing\n## Numerical Methods for Partial Differential Equations\n## Machine Learning and Surrogate Modeling\n## Numerical Optimization\n# UV-curable Inks in Additive Manufacturing\n## Introduction\n## Experimental Methods & Materials\n## Results & Discussion\n## Applications\n## Conclusion\n# Modeling and Simulation of UV-Curable Materials\n## Introduction\n## Thermo-Chemical Continuum Formulation\n## Numerical Example: UV kinetics for UGAP\n## Summary and Extensions\n# Computational Optimization for Material Design\n## Introduction\n## Materials Optimization Strategy\n## No Free Lunch\n## Applications to Advanced Manufacturing\n## Designing the Multi-Objective Function\n## Bayesian Optimization\n## Covariance Matrix Adaptation-Evolutionary Strategy\n## Genetic Algorithms\n## Discussion and Comparison","[{\"question\":\"Why is optimization of chemical properties and processing parameters important in advanced manufacturing?\",\"answer\":\"To achieve desired performance benchmarks while satisfying manufacturing and design constraints. The goal is efficient formulation and parameter selection without excessive repeated trials.\"},{\"question\":\"How does this work improve over traditional design of experiments (DOE)?\",\"answer\":\"It uses faster multi-physics simulations plus machine learning/optimization algorithms to reduce engineering time, cost, and overall development risk compared with intuition-driven iterative DOE.\"},{\"question\":\"How is the proposed framework demonstrated in the dissertation?\",\"answer\":\"By designing acrylate-based UV-curable inks for additive manufacturing, using multi-physics modeling as a virtual environment and methods like Evolutionary Strategies and Bayesian Optimization to search for optimal input parameters within experimentally determined constraints.\"}]","Simulation-Informed Optimization and Machine Learning for Advanced Manufacturing | PDF",1785728206,517,{"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},"simulation-informed-optimization-and-machine-learning-for-advanced-manufacturing","",{"@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/simulation-informed-optimization-and-machine-learning-for-advanced-manufacturing/120103/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is optimization of chemical properties and processing parameters important in advanced manufacturing?","Question",{"text":75,"@type":76},"To achieve desired performance benchmarks while satisfying manufacturing and design constraints. The goal is efficient formulation and parameter selection without excessive repeated trials.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does this work improve over traditional design of experiments (DOE)?",{"text":80,"@type":76},"It uses faster multi-physics simulations plus machine learning/optimization algorithms to reduce engineering time, cost, and overall development risk compared with intuition-driven iterative DOE.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed framework demonstrated in the dissertation?",{"text":84,"@type":76},"By designing acrylate-based UV-curable inks for additive manufacturing, using multi-physics modeling as a virtual environment and methods like Evolutionary Strategies and Bayesian Optimization to search for optimal input parameters within experimentally determined constraints.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]