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 Selective laser melting (SLM) process offers a versatile additive manufacturing technology. However, the wide range of process parameters and complex thermophysical phenomenon necessitate optimization of process parameters for obtaining a high‐quality finished product. The optimization of process parameters through experiment is expensive and time‐consuming. On the other hand, computational approaches offer a fast and economical way to predict the contributions of process parameters. In this article, therefore, a multiphysics finite‐element model and phase‐filed model of solidification process were used to investigate the effects of process parameters on the melt pool solidification parameters and microstructure evolution of SLM‐Ti6Al4V process. Simulations were performed using the single‐level setup method followed by a parametric sweep optimization (PSO) approach that helped identify the best‐suited process parameters. Through the PSO, reductions in temperature gradient by 9.7% and 13.7%, and cooling rate by 23.6% and 14.3% were found at a fixed laser scan speed and laser power, respectively. The associated solidification morphology factor was found to be 5.8 × 10 5 Ks/m 2 . In addition, the primary dendrite arm spacing (PDAS) was found to be at 3.6% and 6.8% increments at a fixed laser speed and laser power, respectively. Finally, optimal results of the solidification parameters were compared with the existing data to validate the approach. The simulation results have been shown that reduction in the temperature gradient by 28.5%, cooling rate by 48.6%, and solidification morphology factor by 3.3% tend to minimize fluctuation of melt pool. The comparisons have also shown that the PSO approach is effective and accurate for predicting the solidification behaviors of SLM‐Ti6Al4V process.
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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