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Multi-Scale Simulation of Heat Affected Zone Development and Part Formation in a Microscale Selective Laser Sintering System
Multi-Scale Simulation of Heat Affected Zone Development and Part Formation in a Microscale Selective Laser Sintering System
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153109
- ISBN
- 9798384442219
- DDC
- 621.3
- 저자명
- Grose, Joshua.
- 서명/저자
- Multi-Scale Simulation of Heat Affected Zone Development and Part Formation in a Microscale Selective Laser Sintering System
- 발행사항
- [Sl] : The University of Texas at Austin, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 153 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Cullinan, Michael.
- 학위논문주기
- Thesis (Ph.D.)--The University of Texas at Austin, 2024.
- 초록/해제
- 요약Existing metal Additive Manufacturing (AM) tools suffer from limitations on the minimum feature sizes of their producible parts. The Microscale Selective Laser Sintering (μ-SLS) system directly addresses this restriction with a minimum part resolution on the order of a single micrometer. However, the production of parts at the micrometer scale is unreliable due to uncontrolled heat transfer in the nanoparticle bed. The formation of Heat Affected Zones (HAZ) in response to unwanted heat conduction blurs the boundary of printed parts, limiting the minimum achievable feature sizes. A multiscale thermal modeling framework is developed in this work to predict and correct for unwanted heat transfer and part formation during sintering. Particle models are first used to simulate the diffusion and material property evolution experienced by copper nanoparticles during sintering. These particle models produce relationships for densification and thermal conductivity change as functions of sintering time and temperature. These functional relationships are incorporated into a part-scale Finite Element (FE) thermal model capable of accurately predicting temperature changes and part formation in the nanoparticle bed during sintering. A machine learning (ML) based regression model is then trained on input-output data generated by the part-scale model to enable rapid and accurate temperature and part-shape predictions 40x faster than FE models. Predictions from this modeling framework will guide a model-based control process used to optimize system inputs, reduce HAZ formation, and improve sintered part resolution.
- 일반주제명
- Computer engineering
- 일반주제명
- Industrial engineering
- 일반주제명
- Nanoscience
- 키워드
- Finite Element
- 키워드
- Machine learning
- 키워드
- HAZ formation
- 기타저자
- The University of Texas at Austin Mechanical Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384442219
■035 ▼a(MiAaPQ)AAI31690577
■035 ▼a(MiAaPQ)123vireo24719Grose
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aGrose, Joshua.
■24510▼aMulti-Scale Simulation of Heat Affected Zone Development and Part Formation in a Microscale Selective Laser Sintering System
■260 ▼a[Sl]▼bThe University of Texas at Austin▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a153 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Cullinan, Michael.
■5021 ▼aThesis (Ph.D.)--The University of Texas at Austin, 2024.
■520 ▼aExisting metal Additive Manufacturing (AM) tools suffer from limitations on the minimum feature sizes of their producible parts. The Microscale Selective Laser Sintering (μ-SLS) system directly addresses this restriction with a minimum part resolution on the order of a single micrometer. However, the production of parts at the micrometer scale is unreliable due to uncontrolled heat transfer in the nanoparticle bed. The formation of Heat Affected Zones (HAZ) in response to unwanted heat conduction blurs the boundary of printed parts, limiting the minimum achievable feature sizes. A multiscale thermal modeling framework is developed in this work to predict and correct for unwanted heat transfer and part formation during sintering. Particle models are first used to simulate the diffusion and material property evolution experienced by copper nanoparticles during sintering. These particle models produce relationships for densification and thermal conductivity change as functions of sintering time and temperature. These functional relationships are incorporated into a part-scale Finite Element (FE) thermal model capable of accurately predicting temperature changes and part formation in the nanoparticle bed during sintering. A machine learning (ML) based regression model is then trained on input-output data generated by the part-scale model to enable rapid and accurate temperature and part-shape predictions 40x faster than FE models. Predictions from this modeling framework will guide a model-based control process used to optimize system inputs, reduce HAZ formation, and improve sintered part resolution.
■590 ▼aSchool code: 0227.
■650 4▼aComputer engineering
■650 4▼aIndustrial engineering
■650 4▼aNanoscience
■653 ▼aAdditive Manufacturing
■653 ▼aHeat Affected Zones
■653 ▼aFinite Element
■653 ▼aMachine learning
■653 ▼aHAZ formation
■690 ▼a0565
■690 ▼a0464
■690 ▼a0800
■690 ▼a0546
■71020▼aThe University of Texas at Austin▼bMechanical Engineering.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0227
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164977▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
