Quantitative prediction of the aged state of Ni-base superalloys using PCA and tensor regression

Gorgannejad, Sanam; Gahrooei, Mostafa Reisi; Paynabar, Kamran; Neu, Richard W.

doi:10.1016/j.actamat.2018.11.047

Cited by 30 publications

(6 citation statements)

References 45 publications

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“…We also linked the environmental conditions of the materials and the structure and properties of microstructures. Previous authors [23] proposed a method for quantifying and predicting the aging state of nickel-based superalloys using PCA and tensor regression. SEM images (83 sets) of cut surfaces of CMSX-8, a single-crystal nickel-based superalloy, were quantified through creep testing under seven different temperature and stress treatment conditions.…”

Section: Related Workmentioning

confidence: 99%

“…Since the dimension of the descriptor is very large, there is a possibility that the prediction model can suffer from overfitting or that the model robustness against outliers of raw data may be poor. In addition, since general regression models are based on frequentism, the predictive power of the model may be lower when learning a small amount of data, and it may be difficult to predict unobserved points [23]. Other authors [24] proposed a method to evaluate the service conditions of a turbine blade made of a directionally solidified nickel-based superalloy through an artificial neural network.…”

Section: Related Workmentioning

confidence: 99%

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AI-Based Degradation Index from the Microstructure Image and Life Prediction Models Based on Bayesian Inference

2023

Sustainability

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In this study, we propose a consistent and explainable degradation indexing method and a non-destructive-based degradation and creep-life prediction method from extensive destructive test (creep-rupture) data of a nickel-based superalloy (DA-5161 SX), an extreme-environment material. High-temperature components made of nickel-based superalloys that operate in extreme environments (e.g., gas turbine blades) deteriorate over time and shorten the life of the device. To ensure the safety and efficiency of the equipment, it is important to predict the lifetime of high-temperature parts, and a consistent and explanatory degradation index and a reliable predictive model that can predict the degree of degradation and life without destructive testing of high-temperature parts are needed. As the degradation of nickel-based superalloys progresses, degradation indices reflecting the geometrical characteristics are required that focus on the fact that the shape of the gamma-prime phase becomes longer and larger. A representative value of the degradation index was selected through parameter inference based on a Bayesian method, and the high-dimensional degradation index of previous studies was simplified to only one dimension. The robustness of the degradation index quantification model was verified by confirming that the degradation index obtained from 20% of the test images had the lowest change rate of the degradation index obtained from 80% of the training images at 6.9%. The basis for predicting the life of high-temperature parts without destructive testing was established in the degradation index and life prediction model by connecting environmental conditions and degradation indices/the LMP (Larson–Miller parameter) to represent creep life in regression models. Gaussian process regression (GPR) models based on sampling-based Bayesian inference performed well in terms of both RMSE in the degradation index and the LMP prediction model, demonstrating robust behavior in performance variation. This may be used as a key health factor that indicates the soundness of diagnostic solutions in the future, and it is expected to be a foundational technology for decision-making models for maintenance, repair, and disposal.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

AI-Based Degradation Index from the Microstructure Image and Life Prediction Models Based on Bayesian Inference

2023

Sustainability

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“…In addition, obtaining the majority of descriptors require intensive experimental, computational, and characterization procedures that demand substantial cost and time in order to achieve a robust and reliable relation. [ 8 ] Therefore, it is necessary to build a robust and rigorous microstructure quantification framework to extract microstructure information quantitatively.…”

Section: Instructionmentioning

confidence: 99%

Symbolic Regression and Two‐Point Statistics Assisted Structure‐Property Linkage Based on Irregular‐Representative Volume Element

Chen

Zhao

et al. 2022

Advcd Theory and Sims

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Quantifying the microstructure of materials is of significance in material development, especially for building the relationship between structure and property. To establish a remarkable structure‐property (SP) linkage, a novel concept referred to as irregular‐representative volume element (IRVE) based on panoramic image stitching technology (PIST) is proposed and a data‐driven scheme integrating irregular domain‐oriented two‐point statistics, principal component analysis (PCA), and symbolic regression based on genetic programming (GPSR) is constructed. Combining with advanced image processing and genetic programming technologies, this scheme improves the microstructure quantization framework. This scheme can not only be applied in different complex conditions for extracting the information of a material microstructure, but can also to embody details of microstructure from the perspective of large scale. IRVE is demonstrated to have both strong statistical representativeness and sufficient physical interpretation, which makes the scheme robust and reliable. Performing the scheme on an example of ferrite heat‐resistant steels, it shows a powerful ability in building an equational SP linkage with high precision (R = 0.91, RMSE = 13.17), the generalization ability of the linkage is also validated by an unseen steel (relative percentage error is 2.66%). The scheme has bright application prospects in predicting mechanical property and accelerating alloy design.

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“…MLR has the advantages of simple structure and no need for parameter adjustment, but it has the disadvantages of low prediction accuracy and long computational time [4]. Besides MLR, Principal Component Analysis (PCA) is widely used in energy consumption prediction models [5].…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning-based approach for residential building heating energy prediction and optimization

Zhang

Huang

Cheng

et al. 2023

Journal of Building Engineering

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Quantitative prediction of the aged state of Ni-base superalloys using PCA and tensor regression

Cited by 30 publications

References 45 publications

AI-Based Degradation Index from the Microstructure Image and Life Prediction Models Based on Bayesian Inference

AI-Based Degradation Index from the Microstructure Image and Life Prediction Models Based on Bayesian Inference

Symbolic Regression and Two‐Point Statistics Assisted Structure‐Property Linkage Based on Irregular‐Representative Volume Element

Ensemble learning-based approach for residential building heating energy prediction and optimization

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