A Predictive Model for Weld Properties in AA-7075-FSW: A Heterogeneous AMIS-Ensemble Machine Learning Approach-Reference-Cited by-同舟云学术

A Predictive Model for Weld Properties in AA-7075-FSW: A Heterogeneous AMIS-Ensemble Machine Learning Approach

Published:2023-09 Issue: Volume:19 Page:200259
ISSN:2667-3053
Container-title:Intelligent Systems with Applications
language:en
Short-container-title:Intelligent Systems with Applications

Author:

Matitopanum Surasak,Luesak Peerawat,Chiaranai Somphop^ORCID,Pitakaso Rapeepan,Srichok Thanatkij^ORCID,Sirirak Worapot,Jirasirilerd Ganokgarn

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,Computer Vision and Pattern Recognition,Signal Processing,Computer Science (miscellaneous)

Reference41 articles.

1. Optimization of friction stir welding process parameters of dissimilar AA2024-T3 and AA7075-T73 aluminum alloys by using taguchi method;Abbass;Al-Khwarizmi Engineering Journal,2016

2. Optimization of process parameters for friction stir welding of dissimilar aluminum alloys using different Taguchi arrays;Abd Elnabi;The International Journal of Advanced Manufacturing Technology,2022

3. FSSW process parameter optimization for AA2024 and AA7075 alloy;Anton Savio Lewise;Materials and Manufacturing Processes,2022

4. Design choice and machine learning model performances;Arboretti;Quality and Reliability Engineering International,2022

5. A PARAMETRIC STUDY ON THE EFfect of FSW parameters and the tool geometry on the tensile strength of AA2024–AA7075 joints: microstructure and fracture;Beygi;Lubricants,2023

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1. Machine learning metamodels for thermo-mechanical analysis of friction stir welding;International Journal on Interactive Design and Manufacturing (IJIDeM);2024-05-25

2. An embedded machine learning strategy for analyzing interfacial characteristics in impact welding of dissimilar alloys;Composite Interfaces;2023-12-21