Fast distortion prediction in directed energy deposition using inversely-identified inherent strains

Abstract

Large thermal distortions due to the cyclic rapid melting and solidification mechanism in metal additive manufacturing (MAM) affect part manufacturing precision. The so-called inherent strain method is one of the most computationally efficient methods to predict distortion, but the current inherent strain methods extract inherent strains through the full-scale detailed thermo-mechanical model (TMM) with a long computation time. This work proposes an inverse parameter identification method to fast identify inherent strains based on the measured distortion results from a two-track-two-layer workpiece. The identified inherent strains are employed in a static mechanical analysis to efficiently predict distortion in MAM. To verify the proposed method, a multi-track-multi-layer workpiece and a square-shaped workpiece deposited by the directed energy deposition process are studied. The simulated distortion results demonstrate high simulation accuracy by comparing with the experimental results. In addition, comparisons with the TMM and the modified inherent strain method indicate that the inversely-identified inherent strains can improve the distortion simulation accuracy and reduce the simulation time, which is practical to be applied in industrial applications.

KEYWORDS:

• Metal additive manufacturing
• distortion prediction
• inverse parameter identification
• inherent strain

Acknowledgements

This study was jointly supported by the National Natural Science Foundation of China (No. 51975495, 51905461), and the Open Research Fund of State Key Laboratory of High-Performance Complex Manufacturing, Central South University. Comments and suggestions from reviewers are greatly appreciated.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was jointly supported by the National Natural Science Foundation of China [grant numbers 51975495; 51905461], and the Open Research Fund of State Key Laboratory of High-Performance Complex Manufacturing, Central South University, China [grant number Kfkt2021-10].