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Simulation-Informed Optimization and Machine Learning for Advanced Manufacturing
Simulation-Informed Optimization and Machine Learning for Advanced Manufacturing
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152711
- ISBN
- 9798384448310
- DDC
- 530
- 서명/저자
- Simulation-Informed Optimization and Machine Learning for Advanced Manufacturing
- 발행사항
- [Sl] : University of California, Berkeley, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 205 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Zohdi, Tarek.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- 초록/해제
- 요약In 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.This 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 demonstrates how these components interact and inform each other in the context of designing acrylate-based 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 development across various industrial applications.
- 일반주제명
- Computational physics
- 일반주제명
- Computer science
- 일반주제명
- Applied mathematics
- 일반주제명
- Mechanical engineering
- 기타저자
- University of California, Berkeley Mechanical Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152711
■006m o d
■007cr#unu||||||||
■020 ▼a9798384448310
■035 ▼a(MiAaPQ)AAI31488695
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aHowell, Brian Matthew.
■24510▼aSimulation-Informed Optimization and Machine Learning for Advanced Manufacturing
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a205 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Zohdi, Tarek.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2024.
■520 ▼aIn 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.This 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 demonstrates how these components interact and inform each other in the context of designing acrylate-based 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 development across various industrial applications.
■590 ▼aSchool code: 0028.
■650 4▼aComputational physics
■650 4▼aComputer science
■650 4▼aApplied mathematics
■650 4▼aMechanical engineering
■653 ▼aMaterials discovery
■653 ▼aMaterials optimization
■653 ▼aMulti-physics simulation
■653 ▼aOptimization algorithms
■653 ▼aDesign of experiments
■690 ▼a0216
■690 ▼a0984
■690 ▼a0364
■690 ▼a0548
■71020▼aUniversity of California, Berkeley▼bMechanical Engineering.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163461▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
