메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
Minghuang Zhao (Beijing University of Chemical Technology) Chenghong Duan (Beijing University of Chemical Technology) Xiangpeng Luo (Beijing University of Chemical Technology)
저널정보
대한금속·재료학회 Metals and Materials International Metals and Materials International Vol.28 No.9
발행연도
2022.9
수록면
2,225 - 2,238 (14page)
DOI
10.1007/s12540-021-01129-w

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
In this study, a three-dimensional finite element analysis (FEA) model is established using user-defined subroutines based oncommercial software ABAQUS to investigate the heat transfer and temperature field distribution during selective laser melting(SLM) additive manufacturing of H13 hot working tool steel powder. The influence of the applied laser volumetric energydensity (VED) on the laser remelting and premelting behaviors during SLM process are quantitatively investigated. And theSLM experiments were carried out to analyze the metallurgical bonding quality. The relationships are explored among theVED, laser remelting/premelting and metallurgical bonding behavior, thereby revealing the formation and regulation mechanismsof metallurgical bonding. The results show that the laser remelting and premelting contribute to improving the lap ratioof molten tracks and promoting the effective metallurgical bonding. The peak temperature of the remolten pool, remeltingand premelting dimensions, remelting and premelting indexes, and lap ratio are positively correlated with the applied VED. Under the optimized VED of 111.1 J/mm3, there is no obvious defect on the cross-section of SLM-fabricated H13 samples,implying a good metallurgical bonding is obtained. This study could provide theoretical and experimental basis for theregulation and control of metallurgical bonding quality of SLM-fabricated parts, which would become design guidelines.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0