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The Integration of ANN and FEA and Its Application to Property Prediction of Dual-Performance Turbine Disks

التفاصيل البيبلوغرافية
العنوان: The Integration of ANN and FEA and Its Application to Property Prediction of Dual-Performance Turbine Disks
المؤلفون: Yanqing Li, Ziming Zhang, Junyi Cheng, Zhaofeng Liu, Chao Yin, Chao Wang, Jianzheng Guo
المصدر: Materials, Vol 17, Iss 13, p 3045 (2024)
بيانات النشر: MDPI AG, 2024.
سنة النشر: 2024
المجموعة: LCC:Technology
LCC:Electrical engineering. Electronics. Nuclear engineering
LCC:Engineering (General). Civil engineering (General)
LCC:Microscopy
LCC:Descriptive and experimental mechanics
مصطلحات موضوعية: artificial neural network, dual microstructure heat treatment, finite element, Ni based super alloy, turbine disks, Technology, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Engineering (General). Civil engineering (General), TA1-2040, Microscopy, QH201-278.5, Descriptive and experimental mechanics, QC120-168.85
الوصف: Regulating the microstructure of powder metallurgy (P/M) nickel-based superalloys to achieve superior mechanical properties through heat treatment is a prevalent method in turbine disk design. However, in the case of dual-performance turbine disks, the complexity and non-uniformity of the heat treatment process present substantial challenges. The prediction of yield strength is typically derived from the analysis of microstructures under various heat treatment regimes. This method is time-consuming, expensive, and the accuracy often depends on the precision of microstructural characterization. This study successfully employed a coupled method of Artificial Neural Network (ANN) and finite element analysis (FEA) to reveal the relationship between the heat treatment process and yield strength. The coupled method accurately predicted the location specified and temperature-dependent yield strength based on the heat treatment parameters such as holding temperatures and cooling rates. The root mean square error (RMSE) and mean absolute percentage deviation (MAPD) for the training set are 50.37 and 3.77, respectively, while, for the testing set, they are 50.13 and 3.71, respectively. Furthermore, an integrated model of FEA and ANN is established using a Abaqus user subroutine. The integrated model can predict the yield strength based on temperature calculation results and automatically update material properties of the FEA model during the loading process simulation. This allows for an accurate calculation of the stress–strain state of the turbine disk during actual working conditions, aiding in locating areas of stress concentration, plastic deformation, and other critical regions, and provides a novel reliable reference for the rapid design of the turbine disk.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1996-1944
Relation: https://www.mdpi.com/1996-1944/17/13/3045; https://doaj.org/toc/1996-1944
DOI: 10.3390/ma17133045
URL الوصول: https://doaj.org/article/1f7179e44a034b3babbc70852ad952cf
رقم الأكسشن: edsdoj.1f7179e44a034b3babbc70852ad952cf
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:19961944
DOI:10.3390/ma17133045