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
Yuhong Lin (Central South University) Xiaohui Cui (Central South University) Kanghua Chen (Central South University) Ang Xiao (Central South University) Ziqin Yan (Central South University)
저널정보
대한금속·재료학회 Metals and Materials International Metals and Materials International Vol.28 No.10
발행연도
2022.10
수록면
2,472 - 2,482 (11page)
DOI
10.1007/s12540-021-01128-x

이용수

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

초록· 키워드

오류제보하기
The effect of electromagnetic forming (EMF) on the forming limit and properties of 2024-O aluminum alloy is studied in thispaper. This was done to address the important problems related to the poor forming limit of aluminum alloy when conventionalstamping is used. The evolution of the microstructure of the alloy during quasi-static stamping (QS) and the dynamicdeformation is analyzed. This was done using mechanical testing, texture analysis, scanning electron microscopy (SEM),fracture analysis, and transmission electron microscopy (TEM). Compared with QS, the forming limit for EMF increases by36.9%. For the same deformation height with 17.6mm, the maximum degree of thickness thinning of the sample for EMF is4.7%, and 6.4% for QS. The thickness distribution of the EMF sample is more uniform than for the QS sample. Numericalsimulation shows the maximum principal stresses at different points were almost same with each other after EMF, whichleads to uniformity plastic deformation of samples. In addition, the grain size of the material decreases, the proportion ofsmall-angle grains increases, and the copper texture increases after EMF. When EMF is used, the dislocation density of thesample is significantly higher than for QS and the dislocation distribution is more uniform. The temperature rise is small,which is not a significant reason for dislocation dispersed in EMF.

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