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Hybrid Polymer Composite Tensile Strength Estimation using K-Nearest Neighboring Classification Algorithm | ||
| Mechanics of Advanced Composite Structures | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 26 تیر 1404 اصل مقاله (607.26 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22075/macs.2025.36748.1798 | ||
| نویسندگان | ||
| Vijaykumar S Jatti1؛ Neeta Deshpande2؛ A Saiyathibrahim3؛ K Balaji* 4 | ||
| 1Department of Mechanical Engineering, School of Engineering and Applied Sciences, Bennett University, Greater Noida, UP, India | ||
| 2R.H. SAPAT College of Engineering, Management Studies and Research, Maharashtra, India | ||
| 3University Centre for Research and Development, Chandigarh University, Punjab, India | ||
| 4Associate Professor, Department of Aeronautical Engineering, Parul Institute of Engineering and Technology, Parul University, Vadodara, India | ||
| تاریخ دریافت: 11 بهمن 1403، تاریخ بازنگری: 16 تیر 1404، تاریخ پذیرش: 26 تیر 1404 | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Acrylonitrile Butadiene Styrene؛ Copper؛ k-nearest neighbor؛ Surfactant | ||
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آمار تعداد مشاهده مقاله: 39 تعداد دریافت فایل اصل مقاله: 35 |
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