Data-driven Bayesian model-based prediction of fatigue crack nucleation in Ni-based superalloys

Pinz, Maxwell; Weber, George; Stinville, J.C.; Pollock, Tresa M.; Ghosh, Somnath

doi:10.1038/s41524-022-00727-5

Cited by 21 publications

(13 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Figure , the images obtained from the scanning electron microscope and electron backscatter diffraction were analyzed to characterize the features of FCGR. [ 172 ] As shown in Figure , a pixel‐level fatigue crack prediction method with simple structure was proposed through an image database obtained from the fatigue test via the encoder–decoder network. [ 66 ]…”

Section: Progress In Prediction Approachesmentioning

confidence: 99%

“…[77] to characterize the features of FCGR. [172] As shown in Figure 23, a pixel-level fatigue crack prediction method with simple structure was proposed through an image database obtained from the fatigue test via the encoder-decoder network. [66] In summary, the characteristics of fatigue crack initiation and propagation are the main differences of fatigue behavior of different components.…”

Section: Characteristics Referencesmentioning

confidence: 99%

“…Modules for developing the data-driven crack nucleation model. [172] Figure 23. Prediction of fatigue crack based on an image database.…”

Section: Characteristics Referencesmentioning

confidence: 99%

See 2 more Smart Citations

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

View full text Add to dashboard Cite

Fatigue fracture of welded joints is an important cause for engineering accidents. Due to the coexistence of so many influencing factors, the current prediction model of fatigue behavior cannot be used for all service conditions. The progress of each module (database establishment, data processing, data augmentation, dimension reduction, and comprehensive consideration of influencing factors) during the process of constructing a prediction model is summarized herein. Specifically, the state‐of‐art prediction models of fatigue strength, fatigue crack growth rate, fatigue life, and fatigue reliability are introduced. Moreover, suggestions for future work are given at the end of each section. Finally, according to the influential prediction methods, the establishment of fatigue performance prediction methods of welded joints via data‐driven method is summarized.

show abstract

Section: Progress In Prediction Approachesmentioning

confidence: 99%

Section: Characteristics Referencesmentioning

confidence: 99%

See 1 more Smart Citation

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

View full text Add to dashboard Cite

show abstract

“…Material innovation has always played an essential role in the science and technology revolution. [1][2][3] In the discovery and design of next-generation materials, artificial intelligence (AI) technology appears to be one of the most promising approaches. [4][5][6][7] Presently, AI technologies are focused on developing predictive functions through a data-driven approach.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Guided Thermoelectric Materials Design and Discovery

Han

Sun

Feng

et al. 2023

Adv Elect Materials

View full text Add to dashboard Cite

Materials discovery from the infinite earth repository is a major bottleneck for revolutionary technological progress. This labor-intensive and time-consuming process hinders the discovery of new materials. Although machine learning techniques show an excellent capability for speeding up materials discovery, obtaining effective material feature representations is still challenging, and making a precise prediction of the material properties is still tricky. This work focuses on developing an automatic material design and discovery framework enabled by data-driven artificial intelligence (AI) models. Multiple types of material descriptors are first developed to promote the representation and encoding of the materials' uniqueness, resulting in improved performance for different molecular properties predictions. The material's thermoelectric (TE) properties prediction is then utilized as a baseline to demonstrate the investigation logistic. The proposed framework achieves more than 90% accuracy for predicting materials' TE properties. Furthermore, the developed AI models identify 6 promising p-type TE materials and 8 promising n-type TE materials. The prediction results are evaluated by density functional theory calculations and agree with the material's TE property provided by experimental results. The proposed framework is expected to accelerate the design and discovery of the new functional materials.

show abstract

“…Material innovation has played an essential role in the science and technology revolution [1,2,3]. In the discovery and design for next-generation materials, artificial intelligence (AI) technology appears to be one of the most promising approaches [4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence assisted thermoelectric materials design and discovery

Lü

Han

Sun

et al. 2022

Preprint

View full text Add to dashboard Cite

Materials discovery from infinite earth repository is considered the bottleneck of each revolutionary technological progress. The discovery of the new materials is hindered by the labor-intensive and time-consuming process. Although machine learning techniques shown the excellent capability for speeding up materials discovery, it is still challenging to obtain effective material feature representations and making a precise prediction of the material properties are still challenging. This work focuses on developing an automatic material design and discovery framework enabled by data-driven AI models. We utilize the material's thermoelectric (TE) properties prediction as a baseline to demonstrate the investigation logistic. The framework works in a closed-loop mode. Starting from the well-established TE materials databases, we develop a TE material encoding method and TE material screening model by using machine learning technology. The models show excellent accuracy for TE properties prediction. Furthermore, the developed AI models have identified 6 novel promising p-type TE materials and 8 novel promising n-type TE materials. Evaluated by the Density-functional Theory (DFT) calculation, the prediction results have a good agreement with the realistic material's TE property. The proposed framework well accelerates the design and discovery of the new functional materials.

show abstract

Data-driven Bayesian model-based prediction of fatigue crack nucleation in Ni-based superalloys

Cited by 21 publications

References 61 publications

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Artificial Intelligence Guided Thermoelectric Materials Design and Discovery

Artificial intelligence assisted thermoelectric materials design and discovery

Contact Info

Product

Resources

About