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A Machine Learning Assisted Multi-Scale Study of Damage Evolution Under Mechanical Deformation in Nanostructured Materials
A Machine Learning Assisted Multi-Scale Study of Damage Evolution Under Mechanical Deformation in Nanostructured Materials
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151347
- ISBN
- 9798383282960
- DDC
- 621
- 서명/저자
- A Machine Learning Assisted Multi-Scale Study of Damage Evolution Under Mechanical Deformation in Nanostructured Materials
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 125 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
- 주기사항
- Advisor: Xu, Wenwu;Cai, Shengqiang.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 요약Metals and alloys are the most viable solution for structural application, despite its very limited range of occupancy in the material property space. Metallic composites have long been conceptualized as a way to extend the range of structural properties, albeit with limited success in practice due to their strength-ductility trade-off. In recent years, nano-structuring has also emerged as a promising tool to obtain properties not attainable through alloying or metallic composites due to its unusually large proportion of nano-interfaces, both single phase such as in the nanocrystalline and multi-phase like in Nanocomposites. Hence to materialize the promise of nanostructured materials and incorporate them in the manufacturing process, a modeling technique is required that is based on the atomistic scale deformation mechanism near the nano-interfaces. In this work a comprehensive atomistic study is conducted to identify the key atomistic mechanisms in nano-crystalline and nanocomposite materials and a machine learning assisted multi-scale modeling technique is implemented to understand large scale implication of the atomistic mechanisms. To that end, molecular dynamic simulation is performed to study the effect of nano-interfaces in the nano-crystalline Magnesium (Mg) and the Aluminum-Silicon Carbide (Al-SiC) Metal Matrix Nanocomposites (MMNCs) materials. A series of machine learning based surrogate model is then trained which are subsequently used in a continuum scale model based on Finite Element Method (FEM). The atomistic scale results reveal the anisotropic deformation in nano-crystalline Mg is highly dependent on the grain size. Deformation in Al-SiC MMNC is accommodated through three subsequent mechanisms namely, defect free, dislocation dominant and nano-interface separation. Multiscale modeling reveals that propagation path of the dislocation dominant mechanism correlates closely to the shear-band formation path. The broader implications of the atomistic findings and the multiscale modeling outcome opens up the possibility to device a multiscale modeling framework for nanostructured materials where conventional dislocation theory is not appropriate to upscale atomistic scale information.
- 일반주제명
- Mechanical engineering
- 일반주제명
- Mechanics
- 일반주제명
- Materials science
- 일반주제명
- Nanotechnology
- 키워드
- Machine learning
- 기타저자
- University of California, San Diego Mechanical and Aerospace Engineering (Joint Doctoral with SDSU)
- 기본자료저록
- Dissertations Abstracts International. 86-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151347
■006m o d
■007cr#unu||||||||
■020 ▼a9798383282960
■035 ▼a(MiAaPQ)AAI31242736
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621
■1001 ▼aHasan, Md. Shahrier.
■24512▼aA Machine Learning Assisted Multi-Scale Study of Damage Evolution Under Mechanical Deformation in Nanostructured Materials
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a125 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-01, Section: B.
■500 ▼aAdvisor: Xu, Wenwu;Cai, Shengqiang.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aMetals and alloys are the most viable solution for structural application, despite its very limited range of occupancy in the material property space. Metallic composites have long been conceptualized as a way to extend the range of structural properties, albeit with limited success in practice due to their strength-ductility trade-off. In recent years, nano-structuring has also emerged as a promising tool to obtain properties not attainable through alloying or metallic composites due to its unusually large proportion of nano-interfaces, both single phase such as in the nanocrystalline and multi-phase like in Nanocomposites. Hence to materialize the promise of nanostructured materials and incorporate them in the manufacturing process, a modeling technique is required that is based on the atomistic scale deformation mechanism near the nano-interfaces. In this work a comprehensive atomistic study is conducted to identify the key atomistic mechanisms in nano-crystalline and nanocomposite materials and a machine learning assisted multi-scale modeling technique is implemented to understand large scale implication of the atomistic mechanisms. To that end, molecular dynamic simulation is performed to study the effect of nano-interfaces in the nano-crystalline Magnesium (Mg) and the Aluminum-Silicon Carbide (Al-SiC) Metal Matrix Nanocomposites (MMNCs) materials. A series of machine learning based surrogate model is then trained which are subsequently used in a continuum scale model based on Finite Element Method (FEM). The atomistic scale results reveal the anisotropic deformation in nano-crystalline Mg is highly dependent on the grain size. Deformation in Al-SiC MMNC is accommodated through three subsequent mechanisms namely, defect free, dislocation dominant and nano-interface separation. Multiscale modeling reveals that propagation path of the dislocation dominant mechanism correlates closely to the shear-band formation path. The broader implications of the atomistic findings and the multiscale modeling outcome opens up the possibility to device a multiscale modeling framework for nanostructured materials where conventional dislocation theory is not appropriate to upscale atomistic scale information.
■590 ▼aSchool code: 0033.
■650 4▼aMechanical engineering
■650 4▼aMechanics
■650 4▼aMaterials science
■650 4▼aNanotechnology
■653 ▼aFinite element modeling
■653 ▼aMachine learning
■653 ▼aMetal Matrix Nanocomposites
■653 ▼aMolecular dynamic simulation
■653 ▼aMultiscale modeling
■653 ▼aNanostructured materials
■690 ▼a0548
■690 ▼a0794
■690 ▼a0346
■690 ▼a0652
■71020▼aUniversity of California, San Diego▼bMechanical and Aerospace Engineering (Joint Doctoral with SDSU).
■7730 ▼tDissertations Abstracts International▼g86-01B.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161369▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
