Hybrid Polymer Composite Tensile Strength Estimation Using K-Nearest Neighboring Classification Algorithm

Document Type : Research Article

Authors

1 Symbiosis Skills and Professional University, Kiwale, Pune, Maharashtra, India

2 R.H. SAPAT College of Engineering, Management Studies and Research, Maharashtra, India

3 Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Chennai, Tamil Nadu, 602105, India

4 Department of Aeronautical Engineering, Parul Institute of Engineering and Technology, Parul University, India

Abstract

The aim of this research work is to characterize the tensile strength of ABS-Cu and ABS-Al composites of different proportions of percentage compositions, as well as the incorporation of surfactant material. For the analysis carried out in the present study, the k-Nearest Neighboring (kNN) classification algorithm is used in order to predict the tensile strength of the various compositions of the ABS-Al and ABS-Cu composites. Real data was not used to train the model due to the time-consuming process; instead, they resorted to synthetic data for the classification model, and for the tensile strength data, they were trained and predicted with better results. The kNN classification algorithm of the ABS-Cu predicted the k-value accuracy to be 80% for k=1 and k=2, and 85% for k=3 and k=5. Similarly, the prediction accuracy for the ABS-Al composition yielded the same results: As the value of k is increased, the required percentage of samples is 80% for k=1 and k=2, 85% for k=3, and 90% for k=5, respectively. The kNN classification algorithm model was also successful in predicting tensile strength, with a recall of more than 80% and an F1 score of 90-95%. A higher quantity of copper and aluminium is said to have the ability to improve the tensile strength of the specimens.

Keywords

Main Subjects

References

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Mechanics of Advanced Composite Structures
Volume 13, Issue 2 - Serial Number 28
In Progress
November 2026
Pages 319-338

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