Human-Machine Adaptation in Lean Waste Elimination for Customized Building Manufacturing Systems
Human-Machine Adaptation in Lean Waste Elimination for Customized Building Manufacturing Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152712
- ISBN
- 9798383611869
- DDC
- 658
- 저자명
- Xiong, Ruoxin.
- 서명/저자
- Human-Machine Adaptation in Lean Waste Elimination for Customized Building Manufacturing Systems
- 발행사항
- [Sl] : Carnegie Mellon University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 127 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Tang, Pingbo.
- 학위논문주기
- Thesis (Ph.D.)--Carnegie Mellon University, 2024.
- 초록/해제
- 요약The growing demand for customization in the building manufacturing industry has led to more frequent changeover operations, necessitating precise and effective adjustments to numerous control parameters to ensure production quality and efficiency. These adjustments often result in significant production waste, such as product scraps and stoppages. Currently, highly trained engineers manually develop these processes, seeking optimal parameter combinations to achieve satisfactory outcomes on production lines. On the other hand, the limited availability of experimental data and the high-dimensional search space present challenges for existing computer algorithms, impeding the development of accurate and robust learning models. To address these challenges, this study explores human-machine collaboration strategies to reduce the costs associated with complex manufacturing tuning processes. We designed a controlled virtual process game to systematically benchmark the performance of human engineers and computer algorithms, including Reinforcement Learning and Bayesian Optimization, in process control. Our platform captured and analyzed human-system dynamics across various manufacturing scenarios for operators with different backgrounds, providing insights into operator effectiveness and variability. We implemented and compared the performance of human operators and state-of-the-art algorithms, highlighting their strengths and improvement areas. Additionally, this study establishes an end-to-end framework for decoding cognitive behaviors and proposes a context-aware decision-making support method to improve decision quality and operational efficiency. We proposed and evaluated two distinct human-machine collaboration strategies-explicit and implicit-demonstrating the transformative potential of these approaches. Our findings suggest that a hybrid strategy, integrating human expertise with algorithmic capabilities, can improve the performance of manufacturing process control compared to using algorithms alone.
- 일반주제명
- Industrial engineering
- 키워드
- Human skills
- 키워드
- Process control
- 기타저자
- Carnegie Mellon University Civil and Environmental Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798383611869
■035 ▼a(MiAaPQ)AAI31488744
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a658
■1001 ▼aXiong, Ruoxin.▼0(orcid)0000-0001-8273-8276
■24510▼aHuman-Machine Adaptation in Lean Waste Elimination for Customized Building Manufacturing Systems
■260 ▼a[Sl]▼bCarnegie Mellon University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a127 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Tang, Pingbo.
■5021 ▼aThesis (Ph.D.)--Carnegie Mellon University, 2024.
■520 ▼aThe growing demand for customization in the building manufacturing industry has led to more frequent changeover operations, necessitating precise and effective adjustments to numerous control parameters to ensure production quality and efficiency. These adjustments often result in significant production waste, such as product scraps and stoppages. Currently, highly trained engineers manually develop these processes, seeking optimal parameter combinations to achieve satisfactory outcomes on production lines. On the other hand, the limited availability of experimental data and the high-dimensional search space present challenges for existing computer algorithms, impeding the development of accurate and robust learning models. To address these challenges, this study explores human-machine collaboration strategies to reduce the costs associated with complex manufacturing tuning processes. We designed a controlled virtual process game to systematically benchmark the performance of human engineers and computer algorithms, including Reinforcement Learning and Bayesian Optimization, in process control. Our platform captured and analyzed human-system dynamics across various manufacturing scenarios for operators with different backgrounds, providing insights into operator effectiveness and variability. We implemented and compared the performance of human operators and state-of-the-art algorithms, highlighting their strengths and improvement areas. Additionally, this study establishes an end-to-end framework for decoding cognitive behaviors and proposes a context-aware decision-making support method to improve decision quality and operational efficiency. We proposed and evaluated two distinct human-machine collaboration strategies-explicit and implicit-demonstrating the transformative potential of these approaches. Our findings suggest that a hybrid strategy, integrating human expertise with algorithmic capabilities, can improve the performance of manufacturing process control compared to using algorithms alone.
■590 ▼aSchool code: 0041.
■650 4▼aIndustrial engineering
■650 4▼aEnvironmental engineering
■653 ▼aBuilding manufacturing systems
■653 ▼aHuman skills
■653 ▼aHuman-machine adaptation
■653 ▼aMass customization
■653 ▼aProcess control
■653 ▼aWaste elimination
■690 ▼a0543
■690 ▼a0546
■690 ▼a0775
■71020▼aCarnegie Mellon University▼bCivil and Environmental Engineering.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0041
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163470▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
