Additive manufacturing (AM) process has extensively been used to fabricate metal parts for large variety of applications. Residual stresses are inevitable in the AM process since material experiences heating and cooling cycles. Implementing finite element (FE) analysis tool to predict residual stress distributions could be of great importance in many applications. Developing an FE‐based modeling framework to accurately simulate residual stresses in a reasonably reduced computational time is highly needed. The FE‐based modeling approach presented here to simulate direct metal deposition (DMD) of AISI 304 L aims to significantly reduce computation cost by implementing an adaptive mesh coarsening algorithm integrated with the FE method. Simulations were performed by the proposed approach, and the results were found in good agreement with conventional fine mesh configuration. The proposed modeling framework offers a potential solution to substantially reduce the computational time for simulating the AM process.
Additive manufacturing (AM) has been gaining considerable attention from both industrial and research communities the recent years. Main challenges in AM modeling arise from the accurate estimation of nodal temperature history, distribution of residual stresses and distortion of parts fabricated by AM and also from high computational efforts. Innovative solutions were proposed and implemented to address these issues in the AM processes of metal alloys and also modeling methods were developed to further improve efficiency and accuracy of the process. The current paper provides a short review on the thermomechanical modeling approaches and techniques developed for residual stress and distortion assessment of direct metal deposition (DMD) of AM parts. The beneficial outcomes and shortages of the recent studies in AM modeling were presented and discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.