☆ 4.7 Article

Data-driven optimization of hardness and toughness of high-entropy nitride coatings

CERAMICS INTERNATIONAL (2023)

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 Materials Science, Ceramics

Design of super-hard high-entropy ceramics coatings via machine learning

Xiaoqian Xu et al.

Summary: This study demonstrates that machine learning combined with high-throughput experimental methods can effectively accelerate the screening of ultra-hard high-entropy ceramic coatings, providing a new approach for the design and optimization of multicomponent materials.

CERAMICS INTERNATIONAL (2022)

添加到收藏夹

Article Materials Science, Ceramics

Maximizing the mechanical performance of Ti3AlC2-based MAX phases with aid of machine learning

Xingjun Duan et al.

Summary: In this study, a novel strategy based on the crystal graph convolution neural network (CGCNN) model has been successfully employed to tune the mechanical properties of Ti3AlC2-based MAX phases. The structure-property correlation between A-site substitution and mechanical properties of Ti-3(Al(1-x)A(x))C-2 is established. The results show that the thermodynamic stability, stiffness, thermal shock resistance, and plasticity of Ti3AlC2 can be improved by the substitution of different elements, providing universal guidance for the synthesis of high-performance MAX phases.

JOURNAL OF ADVANCED CERAMICS (2022)

添加到收藏夹

Article Chemistry, Physical

Data-driven analysis of process, structure, and properties of additively manufactured Inconel 718 thin walls

Lichao Fang et al.

Summary: Accurately predicting the temperature field is crucial in additive manufacturing of metal parts. In this study, a finite element simulation and machine learning methods were used to predict the temperature field during the construction process of Inconel 718 walls, and the relationship between temperature field and material properties was investigated.

NPJ COMPUTATIONAL MATERIALS (2022)

添加到收藏夹

Article Materials Science, Multidisciplinary

Machine learning assisted predictions of multi-component phase diagrams and fine boundary information

Jingjin He et al.

Summary: This study proposes a machine learning strategy to accurately predict the phase diagram of a multi-component ferroelectric system. By combining classification and regression methods, the composition and temperature are mapped to the phase. The neural network regression model accurately predicts the phase transition temperatures and establishes the phase diagram. The predicted results are experimentally validated.

ACTA MATERIALIA (2022)

添加到收藏夹

Article Materials Science, Multidisciplinary

Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

Danial Khatamsaz et al.

Summary: This paper presents a novel multi-information BO framework for learning materials design as a multiple objectives and constraints problem. By optimizing ductility indicators and studying manufacturing constraints, the framework efficiently explores a multi-principal-element alloy space.

ACTA MATERIALIA (2022)

添加到收藏夹

Article Materials Science, Multidisciplinary

A new strategy for long-term complex oxidation of MAX phases: Database generation and oxidation kinetic model establishment with aid of machine learning

Chunyu Guo et al.

Summary: In this study, a comprehensive oxidation database of MAX phases was generated using a machine learning model combined with experimental data. The long-term complexity of the oxidation process was accurately addressed, providing guidance for understanding the complex oxidation of other ceramics and alloys.

ACTA MATERIALIA (2022)

添加到收藏夹

Article Materials Science, Multidisciplinary

Accelerated design for magnetic high entropy alloys using data-driven multi-objective optimization

Xin Li et al.

Summary: This work proposes a data-driven method for accelerating the design of magnetic properties in high entropy alloys (HEAs). By utilizing machine learning models and multi-objective optimization algorithms, targeted properties can be predicted and optimal alloy compositions can be searched to meet the desired performance requirements.

JOURNAL OF MATERIALS CHEMISTRY C (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

The importance of interpretability and visualization in machine learning for applications in medicine and health care

Alfredo Vellido

NEURAL COMPUTING & APPLICATIONS (2020)

添加到收藏夹

Article Materials Science, Multidisciplinary

Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening

Hongtao Zhang et al.

ACTA MATERIALIA (2020)

添加到收藏夹

Article Engineering, Multidisciplinary

Deposition of TiN and TiAlVN thin films by DC magnetron sputtering Composition, corrosion and mechanical study

Bassam Abdallah et al.

INTERNATIONAL JOURNAL OF STRUCTURAL INTEGRITY (2020)

添加到收藏夹

Article Materials Science, Multidisciplinary

Commentary on using H/E and H3/E2 as proxies for fracture toughness of hard coatings

Xinjie Chen et al.

THIN SOLID FILMS (2019)

添加到收藏夹

Review Nanoscience & Nanotechnology

High-entropy alloys

Easo P. George et al.

NATURE REVIEWS MATERIALS (2019)

添加到收藏夹

Article Chemistry, Physical

Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

Yuma Iwasaki et al.

NPJ COMPUTATIONAL MATERIALS (2019)

添加到收藏夹

Article Materials Science, Multidisciplinary

The improved corrosion resistance of sputtered CrN thin films with Cr-ion bombardment layer by layer

Desheng Wang et al.

VACUUM (2017)

添加到收藏夹

Article Nanoscience & Nanotechnology

Superlattice effect for enhanced fracture toughness of hard coatings

R. Hahn et al.

SCRIPTA MATERIALIA (2016)

添加到收藏夹

Review Engineering, Chemical

Evaluation techniques and improvements of adhesion strength for TiN coating in tool applications: a review

M. F. Othman et al.

JOURNAL OF ADHESION SCIENCE AND TECHNOLOGY (2015)

添加到收藏夹

Article Engineering, Mechanical

Tribological behavior of AlCoCrCuFeNi and AlCoCrFeNiTi0.5 high entropy alloys under hydrogen peroxide solution against different counterparts

Yuan Yu et al.

TRIBOLOGY INTERNATIONAL (2015)

添加到收藏夹

Article Materials Science, Multidisciplinary

Enhanced electrochemical performance by wrapping graphene on carbon nanotube/sulfur composites for rechargeable lithium-sulfur batteries

Yishan Wu et al.

MATERIALS LETTERS (2014)

添加到收藏夹

Article Multidisciplinary Sciences

A fracture-resistant high-entropy alloy for cryogenic applications

Bernd Gludovatz et al.

SCIENCE (2014)

添加到收藏夹

Article Materials Science, Multidisciplinary

Structural evolution of Ti-Al-Si-N nanocomposite coatings

S. Carvalho et al.

VACUUM (2009)

添加到收藏夹

Article Nanoscience & Nanotechnology

Avoiding the high-temperature decomposition and softening of (Al1-xTix)N coatings by the formation of stable superhard nc-(Al1-xTix)N/a-Si3N4 nanocomposite

S Veprek et al.

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING (2004)

添加到收藏夹

Article Materials Science, Multidisciplinary

Nanostructured high-entropy alloys with multiple principal elements: Novel alloy design concepts and outcomes

JW Yeh et al.

ADVANCED ENGINEERING MATERIALS (2004)

添加到收藏夹

Article Statistics & Probability

Greedy function approximation: A gradient boosting machine

JH Friedman

ANNALS OF STATISTICS (2001)

添加到收藏夹

Article Materials Science, Coatings & Films

Composition, nanostructure and origin of the ultrahardness in nc-TiN/a-Si3N4/a- and nc-TiSi2 nanocomposites with Hv=80 to ≤ 105 GPa

S Veprek et al.

SURFACE & COATINGS TECHNOLOGY (2000)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.