Machine learning aided nanoindentation: A review of the current state and future perspectives
出版年份 2023 全文链接
标题
Machine learning aided nanoindentation: A review of the current state and future perspectives
作者
关键词
-
出版物
CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE
Volume 27, Issue 4, Pages 101091
出版商
Elsevier BV
发表日期
2023-07-02
DOI
10.1016/j.cossms.2023.101091
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- (2022) Yongfeng Li et al. INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
- Hot deformation behavior and microstructure evolution of stainless steel/carbon steel laminated composites
- (2022) Yaohua Yang et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Dynamic compaction of aluminum with nanopores of varied shape: MD simulations and machine-learning-based approximation of deformation behavior
- (2022) Fanil T. Latypov et al. INTERNATIONAL JOURNAL OF PLASTICITY
- Mechanical anisotropy and its evolution with powder reuse in Electron Beam Melting AM of Ti6Al4V
- (2021) R. Schur et al. MATERIALS & DESIGN
- High-speed nanoindentation mapping of a near-alpha titanium alloy made by additive manufacturing
- (2021) Zhiying Liu et al. JOURNAL OF MATERIALS RESEARCH
- Laser deposition of graded γ-TiAl/Ti2AlNb alloys: Microstructure and nanomechanical characterization of the transition zone
- (2021) Haoxiu Chen et al. JOURNAL OF ALLOYS AND COMPOUNDS
- Experimental Investigations of Micro-Meso Damage Evolution for a Co/WC-Type Tool Material with Application of Digital Image Correlation and Machine Learning
- (2021) Yanling Schneider et al. Materials
- High-speed nanoindentation mapping of organic matter-rich rocks: A critical evaluation by correlative imaging and machine learning data analysis
- (2021) S. Vranjes-Wessely et al. INTERNATIONAL JOURNAL OF COAL GEOLOGY
- Machine Learning Classifiers for Surface Crack Detection in Fracture Experiments
- (2021) Adrien Müller et al. INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
- Machine learning predicts fretting and fatigue key mechanical properties
- (2021) Maysam B. Gorji et al. INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
- Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms
- (2021) Stephania Kossman et al. Materials
- Carbon Fiber Reinforced Composites: Study of Modification Effect on Weathering-Induced Ageing via Nanoindentation and Deep Learning
- (2021) Georgios Konstantopoulos et al. Nanomaterials
- Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments
- (2021) I.M.R. Najjar et al. CERAMICS INTERNATIONAL
- Correlation between grain boundary evolution and mechanical properties of ultrafine-grained metals
- (2020) Mohamed Shaat et al. MECHANICS OF MATERIALS
- Extraction of mechanical properties of materials through deep learning from instrumented indentation
- (2020) Lu Lu et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests
- (2020) Alexey Beskopylny et al. Materials
- Testing Novel Portland Cement Formulations with Carbon Nanotubes and Intrinsic Properties Revelation: Nanoindentation Analysis with Machine Learning on Microstructure Identification
- (2020) Georgios Konstantopoulos et al. Nanomaterials
- Application of improved GRNN model to predict interlamellar spacing and mechanical properties of hypereutectoid steel
- (2020) Ling Qiao et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Classification of mechanism of reinforcement in the fiber-matrix interface: Application of Machine Learning on nanoindentation data
- (2020) Georgios Konstantopoulos et al. MATERIALS & DESIGN
- Utilization of Random Vector Functional Link integrated with Marine Predators Algorithm for tensile behavior prediction of dissimilar friction stir welded aluminum alloy joints
- (2020) Mohamed Abd Elaziz et al. Journal of Materials Research and Technology-JMR&T
- High-temperature tensile characteristics and constitutive models of ultrahigh strength steel
- (2020) DongXu Wen et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Deep learning based characterization of nanoindentation induced acoustic events
- (2020) Antanas Daugela et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys
- (2020) David Merayo et al. Materials
- Red fox optimization algorithm
- (2020) Dawid Połap et al. EXPERT SYSTEMS WITH APPLICATIONS
- Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning
- (2019) Xiang Li et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Damage progression in thermal barrier coating systems during thermal cycling: A nano-mechanical assessment
- (2019) Giovanni Bolelli et al. MATERIALS & DESIGN
- Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
- (2019) Max Schwarzer et al. COMPUTATIONAL MATERIALS SCIENCE
- Nanomechanical properties of thermal arc sprayed coating using continuous stiffness measurement and artificial neural network
- (2019) Wai Yeong Huen et al. SURFACE & COATINGS TECHNOLOGY
- Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible
- (2019) Miguel A. Bessa et al. ADVANCED MATERIALS
- Critical assessment of high speed nanoindentation mapping technique and data deconvolution on thermal barrier coatings
- (2019) B. Vignesh et al. MATERIALS & DESIGN
- Modelling of laser powder bed fusion process and analysing the effective parameters on surface characteristics of Ti-6Al-4V
- (2019) Amir Mahyar Khorasani et al. INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
- A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
- (2019) Xuhui Meng et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Material structure-property linkages using three-dimensional convolutional neural networks
- (2018) Ahmet Cecen et al. ACTA MATERIALIA
- Machine learning for molecular and materials science
- (2018) Keith T. Butler et al. NATURE
- Neural network implementation for the prediction of secondary phase precipitation and mechanical feature in a duplex stainless steel
- (2018) Gabriele Baiocco et al. APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING
- High-resolution high-speed nanoindentation mapping of cement pastes: Unravelling the effect of microstructure on the mechanical properties of hydrated phases
- (2016) M. Sebastiani et al. MATERIALS & DESIGN
- Mechanical property measurements of heterogeneous materials by selective nanoindentation: Application to LiMn2O4 cathode
- (2013) Hugues-Yanis Amanieu et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Wear mechanisms and in-service surface modifications of a Stellite 6B Co–Cr alloy
- (2012) M. Sebastiani et al. WEAR
- Artificial Neural Network: Some Applications in Physical Metallurgy of Steels
- (2009) Monideepa Mukherjee et al. MATERIALS AND MANUFACTURING PROCESSES
- A simple and fast algorithm for K-medoids clustering
- (2008) Hae-Sang Park et al. EXPERT SYSTEMS WITH APPLICATIONS
- Prediction of yield stress in highly irradiated ferritic steels
- (2008) Colin G Windsor et al. MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING
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