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Yunbo Hao

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Proceedings Article | 28 June 2023 Paper

Prediction of single-track deposition quality in additive manufacturing of NiTi shape memory alloy

Xu Wang, Yunbo Hao, Kai Zhao, Chunsheng Sha, Weiqi Li, Jiali Gao

Proceedings Volume 12720, 127200P (2023) https://doi.org/10.1117/12.2675008

KEYWORDS: Alloys, Laser soldering, Education and training, Machine learning, Additive manufacturing, Shape memory alloys, Process modeling, Data modeling, Laser processing, Evolutionary algorithms

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Nickel-titanium (NiTi) shape memory alloy (SMA) has excellent application potential in aerospace because of its shape memory effect and super elasticity, but is still limited by poor machinability and weldability in traditional processing techniques. The coaxial powder feeding laser deposition technology opens a new window in the processing of NiTi SMA components. In this paper, prediction models between the process parameters (laser power, scanning speed and powder feeding rate) and process state parameter (melted pool temperature), deposition quality (track width, track height, microhardness) in NiTi alloy laser metal deposition based on the Back Propagation Neural Networks (BPNN) and Random Forest (RF) algorithms were estab-lished. Thirty single tracks were deposited and measured as training groups. The results show that the average prediction error based on the BPNN model for microhardness, track width, track height and melted pool temperature are 0.37%, 1.88%, 4.45% and 0.91%, respectively, which are better than the RF model. Then, BPNN model was further used to predict deposied quality under the combination of five new process parameters groups. Objective of this study was to provide a guidance for the subsequent optimization of process parameters for the improvement of the deposition accuracy of NiTi alloy parts.

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