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- Title
Prediction of Tensile Properties in Inconel 625 Superalloy Fabricated by Wire Arc Additive Manufacturing Using Improved Artificial Neural Network.
- Authors
Xu, Defang; Lu, Zhenhui; Chen, Lei; Zhang, Jian
- Abstract
This study exhibits the possibility of using an artificial neural network (ANN) to model the mechanical behavior of wire arc additive manufacturing (WAAM) for Inconel 625. For this reason, tensile tests of Inconel 625 superalloy as-built (AB) samples and samples after heat treatment at 1200 °C (HT-1200) by WAAM were performed. For the HT-1200 samples, the yield stress decreased, and the elongation increased significantly due to grain refinement and the formation of annealed twins. A new hybrid model combining a swarm intelligence optimization algorithm with a back propagation neural network (BPNN) was developed to simulate the flow behavior of the superalloy. Compared with other hybrid BPNN models that have been reported, the proposed BPNN model is in better agreement with the experimental data and provides a better description of the flow stress of the Inconel 625 superalloy. The excellent predictive ability of the model may be attributed to the optimization of the weights and thresholds of the BPNN, which obtains the optimal global solution in the search space more efficiently.
- Subjects
ARTIFICIAL neural networks; INCONEL; HEAT resistant alloys; OPTIMIZATION algorithms; SWARM intelligence
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 8, p3240
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14083240