4.5 Article

Improving the prediction of mechanical properties of aluminium alloy using data-driven class-based regression

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Multidisciplinary Sciences

Unsupervised machine learning discovers classes in aluminium alloys

Ninad Bhat et al.

Summary: Aluminium alloys are crucial in various applications, but their development has mainly relied on trial-and-error methods and lacked a comprehensive classification. This study used iterative label spreading (ILS), an unsupervised machine learning approach, to identify eight classes of Al alloys based on a curated dataset of 1154 alloys. Validation of class separation was done using a decision tree classifier.

ROYAL SOCIETY OPEN SCIENCE (2023)

Article Multidisciplinary Sciences

Prediction of Mechanical Properties of the 2024 Aluminum Alloy by Using Machine Learning Methods

Hatice Varol Ozkavak et al.

Summary: Al alloys have wide applications but suffer from lower strength compared to steels. Heat treatment, specifically aging heat treatment, is applied to increase their strength. Determining the appropriate temperature and time values requires numerous experiments. Therefore, artificial intelligence methods have been employed to predict the mechanical properties of materials. This study aims to determine the change in mechanical properties of AA 2024 material after aging using machine learning algorithms, such as convolutional neural network (CNN), artificial neural network (ANN), and random forest regression (RFR).

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2023)

Article Materials Science, Multidisciplinary

Design of high strength and electrically conductive aluminium alloys by machine learning

Tingting Liang et al.

Summary: Traditional thermodynamic modeling has limitations in predicting properties of materials, especially when functional and mechanical properties are correlated and dependent on multiple phases in three dimensions. Machine learning coupled with thermodynamic calculations can optimize alloy designs and improve prediction accuracy for new alloys.

MATERIALS SCIENCE AND TECHNOLOGY (2022)

Article

Analysis of Various Machine Learning Algorithms for Cast Aluminium Alloy to Estimate Fatigue Strength

Vedant Shrikant Utpat et al.

Journal of The Institution of Engineers (India): Series D (2022)

Article Materials Science, Multidisciplinary

A feasibility study of machine learning-assisted alloy design using wrought aluminum alloys as an example

Yasaman J. Soofi et al.

Summary: This study focuses on the practicality and reliability of machine learning (ML) in estimating alloy properties using a realistic small dataset of commercial wrought aluminum alloys. Statistical analysis was performed to understand the correlation among compositions, mechanical properties, and technological properties. Several popular ML models were evaluated for predictive performance and the possibility of improving the models through engineering the feature space was explored. The study demonstrates that ML and data mining techniques can aid alloy design on realistic small datasets.

COMPUTATIONAL MATERIALS SCIENCE (2022)

Article Materials Science, Multidisciplinary

Prediction of Mechanical Properties of Wrought Aluminium Alloys Using Feature Engineering Assisted Machine Learning Approach

Mingwei Hu et al.

Summary: A new feature engineering method called procedure-oriented decomposition (POD) was proposed to address the complexity of manufacturing processes in wrought Al alloys, integrating both chemical compositions and manufacturing processes as features. The study established a correlation mapping between these features and the mechanical properties of wrought Al alloys using the support vector regressor (SVR) model, demonstrating high prediction accuracy and the potential to design new alloys.

METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE (2021)

Article Health Care Sciences & Services

Consequences of ignoring clustering in linear regression

Georgia Ntani et al.

Summary: In this study, it was found that failure to account for clustering in linear regression may lead to significantly erroneous conclusions, especially with continuous explanatory variables. The precision of effect estimates from the ordinary least squares (OLS) model was also found to be lower when the explanatory variable was more clustered.

BMC MEDICAL RESEARCH METHODOLOGY (2021)

Review Engineering, Marine

A review on ultimate strength of aluminium structural elements and systems for marine applications

Omid Ferdowsi Hosseinabadi et al.

Summary: Aluminium alloys are widely used in the marine industry, particularly in the construction of high-speed vessels. However, the ultimate strength prediction methods developed for steel structures cannot be directly applied to aluminium alloys due to their different material behavior. Factors such as initial imperfections and boundary conditions have a significant impact on the ultimate strength of aluminium ship hull girder elements.

OCEAN ENGINEERING (2021)

Article Chemistry, Physical

Unsupervised structure classes vs. supervised property classes of silicon quantum dots using neural networks

Amanda J. Parker et al.

Summary: Machine learning classification is a useful tool for predicting structure/property relationships in nanomaterial samples. This study compares domain-driven and data-driven approaches, revealing a disconnect between color representation and actual class classification.

NANOSCALE HORIZONS (2021)

Article Nanoscience & Nanotechnology

Effect of Mg content on age-hardening response, tensile properties, and microstructures of a T5-treated thixo-cast hypoeutectic Al-Si alloy

K. Yamamoto et al.

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING (2020)

Article Physics, Condensed Matter

Neural network model for 7000 (Al-Z) alloys: Classification and prediction of mechanical properties

Adel Belayadi et al.

PHYSICA B-CONDENSED MATTER (2019)

Article Telecommunications

Visible light-based indoor localization using k-means clustering and linear regression

Muhammad Saadi et al.

TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES (2019)

Article Chemistry, Physical

Reliable and explainable machine-learning methods for accelerated material discovery

Bhavya Kailkhura et al.

NPJ COMPUTATIONAL MATERIALS (2019)

Article Multidisciplinary Sciences

Selecting Appropriate Clustering Methods for Materials Science Applications of Machine Learning

Amanda J. Parker et al.

ADVANCED THEORY AND SIMULATIONS (2019)

Article Multidisciplinary Sciences

Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology

Jiaqi Wang et al.

ADVANCED THEORY AND SIMULATIONS (2019)

Article Materials Science, Multidisciplinary

Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys

Saurabh Kumar Gupta et al.

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS (2018)

Article Materials Science, Multidisciplinary

Intelligent design optimization of age-hardenable Al alloys

Swati Dey et al.

COMPUTATIONAL MATERIALS SCIENCE (2018)

Review Engineering, Aerospace

Recent advances in the development of aerospace materials

Xuesong Zhang et al.

PROGRESS IN AEROSPACE SCIENCES (2018)

Article Computer Science, Theory & Methods

Correlation and variable importance in random forests

Baptiste Gregorutti et al.

STATISTICS AND COMPUTING (2017)

Article Chemistry, Physical

Design of novel age-hardenable aluminium alloy using evolutionary computation

Swati Dey et al.

JOURNAL OF ALLOYS AND COMPOUNDS (2017)

Article Physics, Multidisciplinary

Learning physical descriptors for materials science by compressed sensing

Luca M. Ghiringhelli et al.

NEW JOURNAL OF PHYSICS (2017)

Article Computer Science, Artificial Intelligence

New training strategies for neural networks with application to quaternary Al-Mg-Sc-Cr alloy design problems

S. Ganguly et al.

APPLIED SOFT COMPUTING (2016)

Article Materials Science, Multidisciplinary

Computational intelligence based design of age-hardenable aluminium alloys for different temperature regimes

Swati Dey et al.

MATERIALS & DESIGN (2016)

Article Chemistry, Physical

A general-purpose machine learning framework for predicting properties of inorganic materials

Logan Ward et al.

NPJ COMPUTATIONAL MATERIALS (2016)

Review Materials Science, Multidisciplinary

Recent developments in advanced aircraft aluminium alloys

Tolga Dursun et al.

MATERIALS & DESIGN (2014)

Article Materials Science, Multidisciplinary

Effect of Zn addition on microstructure and mechanical properties of an Al-Mg-Si alloy

Lizhen Yan et al.

PROGRESS IN NATURAL SCIENCE-MATERIALS INTERNATIONAL (2014)

Article Nanoscience & Nanotechnology

Grain boundary misorientation dependence of β phase precipitation in an Al-Mg alloy

D. Scotto D'Antuono et al.

SCRIPTA MATERIALIA (2014)

Article Electrochemistry

Modeling pit initiation rate as a function of environment for Aluminum alloy 7075-T651

M. K. Cavanaugh et al.

ELECTROCHIMICA ACTA (2012)

Article Metallurgy & Metallurgical Engineering

Effect of Alloying Elements on the Structure and Properties of Al-Li-Cu Cast Alloys

A. A. Il'in et al.

RUSSIAN METALLURGY (2009)

Article Materials Science, Multidisciplinary

The effect of scandium on the microstructure, mechanical properties and weldability of a cast Al-Mg alloy

S Lathabai et al.

ACTA MATERIALIA (2002)

Article Metallurgy & Metallurgical Engineering

Aluminum alloys: Promising materials in the automotive industry

IN Fridlyander et al.

METAL SCIENCE AND HEAT TREATMENT (2002)

Article Nanoscience & Nanotechnology

Investigation of fine scale precipitates in Al-Zn-Mg alloys after various heat treatments

T Engdahl et al.

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Nanoscience & Nanotechnology

Recent development in aluminium alloys for the automotive industry

WS Miller et al.

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING (2000)

Article Nanoscience & Nanotechnology

Recent development in aluminium alloys for aerospace applications

A Heinz et al.

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING (2000)