Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

Artificial Intelligence Assisted Mid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soils

(2020) Nataly J. Galán-Freyle et al.   Applied Sciences-Basel

Feature Importance of Stabilised Rammed Earth Components Affecting the Compressive Strength Calculated with Explainable Artificial Intelligence Tools

(2020) Hubert Anysz et al.   Materials

Deformation Behavior and Precipitation Features in a Stretched Al–Cu Alloy at Intermediate Temperatures

(2020) Y.C. Lin et al.   Materials

Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data

(2020) David Merayo Fernández et al.   Metals

Prediction of load-displacement curve in a complex structure using artificial neural networks: A study on a long bone

(2020) Hadi Rahmanpanah et al.   INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE

Deep neural network approach to estimate biaxial stress-strain curves of sheet metals

(2020) Akinori Yamanaka et al.   MATERIALS & DESIGN

Online quality inspection of ultrasonic composite welding by combining artificial intelligence technologies with welding process signatures

(2020) Yang Li et al.   MATERIALS & DESIGN

Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning

(2019) Ruofeng Wei et al.   Materials

Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks

(2019) Shuai Teng et al.   Applied Sciences-Basel

Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation

(2019) Nicola Baldo et al.   Applied Sciences-Basel

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design

(2019) Teng Zhou et al.   Engineering

Stress–strain curves of metallic materials and post‐necking strain hardening characterization: A review

(2019) Shengwen Tu et al.   FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES

An Experimental and Numerical Analysis of the Compression of Bimetallic Cylinders

(2019) Ana María Camacho et al.   Materials

Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen

(2017) Titus Thankachan et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Guidelines for Selecting Plugs Used in Thin-Walled Tube Drawing Processes of Metallic Alloys

(2017) et al.   Metals

Artificial neural network application to study quantitative relationship between silicide and fracture toughness of Nb-Si alloys

(2017) Guangxu Liu et al.   MATERIALS & DESIGN

Multi-scale computation methods: Their applications in lithium-ion battery research and development

(2016) Siqi Shi et al.   Chinese Physics B

Onset Frequency of Fatigue Effects in Pure Aluminum and 7075 (AlZnMg) and 2024 (AlCuMg) Alloys

(2016) Jose Rojas et al.   Metals

The Use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of AA5182-O sheets

(2016) Arash Jenab et al.   MATERIALS & DESIGN

ViennaMaterials – A dedicated material library for computational science and engineering

(2015) Josef Weinbub et al.   APPLIED MATHEMATICS AND COMPUTATION

Materials Selection Criteria for Nuclear Power Applications: A Decision Algorithm

(2015) Álvaro Rodríguez-Prieto et al.   JOM

On the representative strain in Vickers hardness testing of 7010 aluminum alloy

(2015) M. Tiryakioğlu et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Hardness–strength relationships in the aluminum alloy 7010

(2015) M. Tiryakioğlu et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Trends in extreme learning machines: A review

(2015) Gao Huang et al.   NEURAL NETWORKS

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Investigations on the influence of blank thickness (t) and length/wide punch ratio (LD) in rectangular deep drawing of dual-phase steels

(2014) José María Gutiérrez Regueras et al.   COMPUTATIONAL MATERIALS SCIENCE

General relationship between strength and hardness

(2011) P. Zhang et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

A procedure for determining the true stress–strain curve over a large range of strains using digital image correlation and finite element analysis

(2011) Masayuki Kamaya et al.   MECHANICS OF MATERIALS

Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images

(2009) Victor Hugo C. de Albuquerque et al.   NDT & E INTERNATIONAL

On the Importance of the Pearson Correlation Coefficient in Noise Reduction

(2008) J. Benesty et al.   IEEE Transactions on Audio Speech and Language Processing

Correlation of Yield Strength and Tensile Strength with Hardness for Steels

(2008) E.J. Pavlina et al.   JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE