Research on hot deformation behavior of Cu-Ti alloy based on machine learning algorithms and microalloying

Zhang, Mengxiao; Chen, Dayong; Liu, Huan; Zhang, Yanyan; Song, Hongwu; Xu, Yong; Zhang, Shihong

doi:10.1016/j.mtcomm.2024.108783

Materials Today Communications

2024

DOI: 10.1016/j.mtcomm.2024.108783

|View full text |Cite

Research on hot deformation behavior of Cu-Ti alloy based on machine learning algorithms and microalloying

Mengxiao Zhang,

Dayong Chen,

Huan Liu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach

Kolev

2024

Modelling

View full text Add to dashboard Cite

A machine learning-based approach is presented for predicting the mechanical properties of Cu-Ti alloys utilizing a dataset of various features, including compositional elements and processing parameters. The features encompass chemical composition elements such as Cu, Al, Ce, Cr, Fe, Mg, Ti, and Zr, as well as various thermo-mechanical processing parameters. This dataset, comprising more than 1000 data points, was selected from a larger collection of various Cu-based alloys. The dataset was divided into training, validation, and test sets, with a Random Forest Regressor model being trained and optimized using GridSearchCV. The model’s performance was evaluated based on the R2 score. The results demonstrate high predictive accuracy, with R2 scores of 0.9929, 0.9851, and 0.9937 for the training, validation, and testing sets, respectively. The Random Forest model was compared with other machine learning models and showed better results in terms of predictive accuracy. A feature importance analysis of the mechanical characteristics was conducted, further clarifying the influence of each feature. The correlation heatmap further elucidates the relationships among the features, offering insights into the effects of alloy composition and processing on mechanical properties. This study underscores the potential of machine learning in advancing the development and optimization of Cu-Ti alloys, providing a valuable tool for materials scientists and engineers.

show abstract

Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach

Kolev

2024

Modelling

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Research on hot deformation behavior of Cu-Ti alloy based on machine learning algorithms and microalloying

Cited by 1 publication

References 17 publications

Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach

Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach

Contact Info

Product

Resources

About