Data–Physics Fusion-Driven Defect Predictions for Titanium Alloy Casing Using Neural Network

Abstract

The quality of Ti alloy casing is crucial for the safe and stable operation of aero engines. However, the fluctuation of key process parameters during the investment casting process of titanium alloy casings has a significant influence on the volume and number of porosity defects, and this influence cannot be effectively suppressed at present. Therefore, this paper proposes a strategy to control the influence of process parameters on shrinkage volume and number. This study constructed multiple regression prediction models and neural network prediction models of porosity volume and number for a ZTC4 casing by simulating the gravity investment casting process. The results show that the multiple regression prediction model and neural network prediction model of shrinkage cavity total volume have an accuracy of over 99%. The accuracy of the neural network prediction model is higher than that of the multiple regression model, and the neural network model realizes the accurate prediction of shrinkage defect volume and defect number through pouring temperature, pouring time, and mold shell temperature. The sensitivity degree of casing defects to key process parameters, from high to low, is as follows: pouring temperature, pouring time, and mold temperature. Further optimizing the key process parameter window reduces the influence of process parameter fluctuation on the volume and number of porosity defects in casing castings. This study provides a reference for actual production control process parameters to reduce shrinkage cavity and loose defects.

Multi-Objective Process Parameter Optimization of Ultrasonic Rolling Combining Machine Learning and Non-Dominated Sorting Genetic Algorithm-II

“…To quickly select the process parameters for laser melting of 316L-Cu multi-material parts with compositional gradients, Rankouhi et al [ 26 ] developed an ML model based on a multivariate GP to predict the density and surface roughness of a finished part with given laser melting process parameters. Yu et al [ 27 ] developed a physics-informed neural network to accurately predict the shrinkage defect volume and the number of defects in titanium alloy shells based on process parameters. The authors further proposed that the predictive model could be used to optimize process parameters.…”