The learning of the precipitates morphological parameters from the composition of nickel-based superalloys

Characterization of γ′ Precipitates in Cast Ni-Based Superalloy and Their Behaviour at High-Homologous Temperatures Studied by TEM and in Situ XRD

(2020) Łukasz Rakoczy et al.   Materials

Neural network model for correlating microstructural features and hardness properties of nickel-based superalloys

(2020) Yangping Li et al.   Journal of Materials Research and Technology-JMR&T

High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel

(2019) Brian L. DeCost et al.   MICROSCOPY AND MICROANALYSIS

Automatic segmentation of liver from CT scans with CCP-TSPM algorithm

(2019) Lifang Zhou et al.   INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE

Machine-learning phase prediction of high-entropy alloys

(2019) Wenjiang Huang et al.   ACTA MATERIALIA

Genetic algorithms for computational materials discovery accelerated by machine learning

(2019) Paul C. Jennings et al.   npj Computational Materials

A property-oriented design strategy for high performance copper alloys via machine learning

(2019) Changsheng Wang et al.   npj Computational Materials

Reliable and explainable machine-learning methods for accelerated material discovery

(2019) Bhavya Kailkhura et al.   npj Computational Materials

Shapley additive explanations for NO2 forecasting

(2019) María Vega García et al.   Ecological Informatics

MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

(2019) Nabil Ibtehaz et al.   NEURAL NETWORKS

Accelerated Discovery of Large Electrostrains in BaTiO3 -Based Piezoelectrics Using Active Learning

(2018) Ruihao Yuan et al.   ADVANCED MATERIALS

Big data analysis for brain tumor detection: Deep convolutional neural networks

(2018) Javeria Amin et al.   Future Generation Computer Systems-The International Journal of eScience

A numerical model to predict mechanical properties of Ni-base disk superalloys

(2018) Young-Kwang Kim et al.   INTERNATIONAL JOURNAL OF PLASTICITY

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

(2018) S. Gorgannejad et al.   ACTA MATERIALIA

A physically based model for correlating the microstructural degradation and residual creep lifetime of a polycrystalline Ni-based superalloy

(2018) Shaolin Li et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Experimental study on micro-grinding force and subsurface microstructure of nickel-based single crystal superalloy in micro grinding

(2017) Yadong Gong et al.   Journal of Mechanical Science and Technology

Design of a nickel-base superalloy using a neural network

(2017) B.D. Conduit et al.   MATERIALS & DESIGN

Image driven machine learning methods for microstructure recognition

(2016) Aritra Chowdhury et al.   COMPUTATIONAL MATERIALS SCIENCE

On the prediction of the yield stress of unimodal and multimodal γ ′ Nickel-base superalloys

(2015) E.I. Galindo-Nava et al.   ACTA MATERIALIA

Deep learning

(2015) Yann LeCun et al.   NATURE

Microstructure evolution in a single crystal nickel-based superalloy joint by linear friction welding

(2015) Tiejun Ma et al.   MATERIALS & DESIGN

Comparison of thermodynamic database models and APT data for strength modeling in high Nb content γ–γ′ Ni-base superalloys

(2015) Stoichko Antonov et al.   MATERIALS & DESIGN

TCP phase predictions in Ni-based superalloys: Structure maps revisited

(2010) B. Seiser et al.   ACTA MATERIALIA

A superior descriptor of random textures and its predictive capacity

(2009) Y. Jiao et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Mechanisms of creep deformation in polycrystalline Ni-base disk superalloys

(2007) R.R. Unocic et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING