Microstructure Property Correlation of Partial Penetration Weld Deposited Stainless Steels for Weld Repair of Investment Castings

Gupta, Rohit; Singh, Saumya Pratap; Dwivedi, Arun Kumar; Agrawal, Alok

doi:10.1007/s12666-023-03026-9

Cited by 1 publication

(1 citation statement)

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“…The large range of size changes and the different thicknesses of the regional organization in the solidification process is often difficult to coordinate. During the subsequent stages of hot isostatic pressing [11], weld repair [12], and heat treatment [13], different shrinkage cavity and loose defects in the same type of castings lead to different casting properties. In addition, the sequence between the inside and outside of the casting during solidification is different.…”

Section: Introductionmentioning

confidence: 99%

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

Yu,

Ji,

Sun

et al. 2024

Materials

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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.

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Section: Introductionmentioning

confidence: 99%