EAAI Journal 2026 Journal Article
Tensile property prediction of titanium and aluminum alloys dissimilar joint by plasma plume characteristics based on a multi-stage cascade model
- Chuang Cai
- Fashuai Xiong
- Zilin Chen
- Hui Chen
- Ping Tang
- Xuanyu Jin
- Zejun Xian
- Hua Tang
In this study, the multi-stage cascade machine learning model was established to predict the titanium and aluminum (Ti/Al) alloys joint tensile property rapidly based on plasma plume characteristics during the welding process. To predict the joint tensile property accurately, the multi-stage modeling strategy was adopted for the establishment of multi-stage cascade model. The three models, plasma plume characteristics and weld cross-sectional size, weld cross-sectional size and zirconium (Zr) element content, Zr element content and joint tensile property, were trained in stages using a backpropagation neural network (BPNN) model, extreme gradient boosting (XGBoost) model, and random forest (RF) model, respectively. The three models were then cascaded and verified. The mean absolute error (MAE) of the predicted value in the cascade model was 0. 0714 kN, and the coefficient of determination (R2) was 0. 9133. The MAE of the predicted value in BPNN stage exhibited the greatest influence on that of the cascade model (ΔMAE1 → 3 = 0. 106). Although the error was transferred and amplified in the multi-stage cascade model, the cascade design of XGBoost and RF models partially absorbed and rebalanced the error. Consequently, the amplification of the error was suppressed and kept within a reasonable range. In addition, compared with the single BPNN model based on plasma plume characteristics the multi-stage cascade model achieved the MAE of 0. 0710 kN (reduced from 0. 6700 kN) and increased the R2 by 45. 3%.