A new method to estimate the residual stresses in additive manufacturing characterized by point heat source
Sun, Li; Ren, Xiaobo; He, Jianying; Olsen, Jim Stian; Pallaspuro, Sakari; Zhang, Zhiliang (2019-10-30)
Sun, L., Ren, X., He, J. et al. A new method to estimate the residual stresses in additive manufacturing characterized by point heat source. Int J Adv Manuf Technol 105, 2415–2429 (2019). https://doi.org/10.1007/s00170-019-04443-1
© Springer-Verlag London Ltd., part of Springer Nature 2019. This is a post-peer-review, pre-copyedit version of an article published in The International Journal of Advanced Manufacturing Technology. The final authenticated version is available online at https://doi.org/10.1007/s00170-019-04443-1.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe202101181999
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Abstract
Residual stress in additive manufacturing (AM) is one of the key challenges in terms of structural integrity and the finish quality of printed components. Estimating the distribution of residual stresses in additively manufactured components is complex and computationally expensive with full-scale thermo-mechanical FE analysis. In this study, a point heat source is utilized to predict the thermal field and residual stress distribution during the manufacturing processes. Numerical results show that the residual stress at a single material point can be expressed as a function of its spatial position and the peak nodal temperature it has experienced during thermal cycles. The distribution of residual stress can be divided into three segments according to the peak nodal temperature. The peak nodal temperature only depends on the heat flux and the distance to the point heat source center. A semi-analytical approach to predict the peak nodal temperature and residual stresses, once the heat flux is known, is proposed. The proposed approach is further validated by a numerical case study, and a very good agreement has been achieved. Compared with traditional thermo-mechanical FE analysis of additive manufacturing, the proposed method significantly improves the computational efficiency, showing great potential for prediction of residual stresses and distortion.
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