인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
ABSTRACT Achieving carbon neutrality in engineering design requires effective emission reduction strategies throughout the entire product lifecycle. Material selection is a key element in this process, as it spans multiple stages and has a decisive impact on total carbon emissions. However, existing material selection methods often rely on static rules or single‐stage assessments, lacking adaptability to complex process design scenarios and the ability to capture cross‐stage interactions. As a result, they fall short of meeting the dynamic and accuracy requirements of real‐world applications. To address this, we propose a lifecycle‐oriented framework, Low‐Carbon Industrial Design Material Selection (LIDMS), for intelligent material selection in engineering applications. The framework integrates a deep learning‐based prediction module, the Multi‐Branch Fusion Module (MFM), with a constraint‐aware combinatorial optimization engine. The MFM captures complex relationships between material properties and design parameters across four lifecycle stages using a stage‐aware attention mechanism. These predictions are embedded into a differentiable optimization model that identifies material combinations minimizing total lifecycle emissions while meeting engineering constraints such as strength, cost, and recyclability. Experimental validation using synthetic data and a real‐world case study (a handheld electric sander casing) demonstrates the method's performance and practical applicability in reducing emissions under realistic constraints. The proposed LIDMS framework offers a scalable solution for sustainable material decision‐making in industrial product and process design. Our code is available at https://github.com/XueFuqiang/Low‐Carbon‐Industrial‐Design‐Material‐Selection.git .
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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