Research on Machine Learning–Based Prediction of Heterogeneous Metal Joining Performance and Its Application in Production and Operations Management

Zhuoxuan Li

doi:10.70393/6a6374616d.343037

OPEN ACCESS |Research Article ||20 March 2026

Research on Machine Learning–Based Prediction of Heterogeneous Metal Joining Performance and Its Application in Production and Operations Management

Zhuoxuan Li

Suzhou University of Science and Technology

Coriander041114@outlook.com

Suzhou University of Science and Technology, CN, Coriander041114@outlook.com.

* Corresponding Author¹: Zhuoxuan Li, E-Mail: Coriander041114@outlook.com

Publication

Accepted 2026 March 20 ; Published 2026 March 20

Journal of Computer Technology and Applied Mathematics, 2026, 3(2), 3007-4126.

Abstract

Dissimilar metal joining technology is a key process for achieving lightweight structures, and accurate prediction of welding quality is crucial for ensuring structural safety. This study constructs a machine learning framework for predicting void defects in friction stir welding (FSW). Using the FSW process dataset (108 records covering three aluminum alloys: AA2219, AA2024, and AA6061), a heat input index is introduced as a derived feature, and SMOTE is applied to address class imbalance. Seven machine learning models are compared under repeated stratified five-fold cross-validation. The results show that MLP achieves the best AUC value (0.8951), followed closely by XGBoost with 0.8912 and stronger stability. This paper further explores the application of the prediction model in quality control and process optimization in a smart manufacturing environment, providing theoretical and practical references for intelligent decision-making in the welding process.

Keywords

Dissimilar Metal Joining , Friction Stir Welding , Machine Learning , Defect Prediction , Production Operations Management .

Metadata

DOI:

10.70393/6a6374616d.343037

ARK:

ark:/40704/JCTAM.v3n2a03

Pages: 21-29

References: 25

Disciplines: Artificial Intelligence

Subjects: Machine Learning

Cite This Article

APA Style

Li, Z. (2026). Research on machine learning–based prediction of heterogeneous metal joining performance and its application in production and operations management. Journal of Computer Technology and Applied Mathematics, 3(2), 21-29. https://doi.org/10.70393/6a6374616d.343037

Acknowledgments

Not Applicable.

FUNDING

Not Applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not Applicable.

DATA AVAILABILITY STATEMENT

Not Applicable.

INFORMED CONSENT STATEMENT

Not Applicable.

CONFLICT OF INTEREST

Not Applicable.

AUTHOR CONTRIBUTIONS

Not application.

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