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Jatti, Vijaykumar Shivashankar, Deshpande, Neeta, Abdulpari, Saiyathibrahim, Karuppiah, Balaji. (1405). Hybrid Polymer Composite Tensile Strength Estimation Using K-Nearest Neighboring Classification Algorithm. هنرهای کاربردی, 13(2), 319-338. doi: 10.22075/macs.2025.36748.1798
Vijaykumar Shivashankar Jatti; Neeta Deshpande; Saiyathibrahim Abdulpari; Balaji Karuppiah. "Hybrid Polymer Composite Tensile Strength Estimation Using K-Nearest Neighboring Classification Algorithm". هنرهای کاربردی, 13, 2, 1405, 319-338. doi: 10.22075/macs.2025.36748.1798
Jatti, Vijaykumar Shivashankar, Deshpande, Neeta, Abdulpari, Saiyathibrahim, Karuppiah, Balaji. (1405). 'Hybrid Polymer Composite Tensile Strength Estimation Using K-Nearest Neighboring Classification Algorithm', هنرهای کاربردی, 13(2), pp. 319-338. doi: 10.22075/macs.2025.36748.1798
Jatti, Vijaykumar Shivashankar, Deshpande, Neeta, Abdulpari, Saiyathibrahim, Karuppiah, Balaji. Hybrid Polymer Composite Tensile Strength Estimation Using K-Nearest Neighboring Classification Algorithm. هنرهای کاربردی, 1405; 13(2): 319-338. doi: 10.22075/macs.2025.36748.1798

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

Mechanics of Advanced Composite Structures
مقاله 5، دوره 13، شماره 2 - شماره پیاپی 28، بهمن 2026، صفحه 319-338    اصل مقاله (1.12 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/macs.2025.36748.1798
نویسندگان
Vijaykumar Shivashankar Jatti1؛ Neeta Deshpande2؛ Saiyathibrahim Abdulpari3؛ Balaji Karuppiah* 4
1Symbiosis Skills and Professional University, Kiwale, Pune, Maharashtra, India
2R.H. SAPAT College of Engineering, Management Studies and Research, Maharashtra, India
3Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Chennai, Tamil Nadu, 602105, India
4Department of Aeronautical Engineering, Parul Institute of Engineering and Technology, Parul University, 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
مراجع
  1. Jatti, V.S., Tamboli, S., Patel, P., Shaikh, S. & Gulia, V., 2024. Optimising the impact strength of 3D printed PLA components using metaheuristic algorithms. Advances in Materials Science, 24(2), p.80. https://doi.org/10.2478/adms-2024-0009
  2. Melentiev, R., Yudhanto, A., Tao, R., Vuchkov, T. & Lubineau, G., 2022. Metallization of polymers and composites: State-of-the-art approaches. Materials & Design, 221, p.110958. https://doi.org/10.1016/j.matdes.2022.110958
  3. Shaban, S.M., Kang, J. & Kim, D.H., 2020. Surfactants: Recent advances and their applications. Composites Communications, 22, p.100537. https://doi.org/10.1016/j.coco.2020.100537
  4. Gong, K., Liu, H., Huang, C., Cao, Z., Fuenmayor, E. & Major, I., 2022. Hybrid manufacturing of acrylonitrile butadiene styrene (ABS) via the combination of material extrusion, additive manufacturing, and injection molding. Polymers, 14(23), p.5093. https://doi.org/10.3390/polym14235093
  5. Shishavan, S.M., Azdast, T. & Ahmadi, S.R., 2014. Investigation of the effect of nanoclay and processing parameters on the tensile strength and hardness of injection molded acrylonitrile butadiene styrene–organoclay nanocomposites. Materials & Design, 58, pp.527–534. https://doi.org/10.1016/j.matdes.2014.02.014
  6. Mohammed, A.J., Mohammed, A.S. & Mohammed, A.S., 2023. Prediction of tribological properties of UHMWPE/SiC polymer composites using machine learning techniques. Polymers, 15(20), p.4057. https://doi.org/10.3390/polym15204057
  7. Esmaeili, H. & Rizvi, R., 2023. An accelerated strategy to characterize mechanical properties of polymer composites using the ensemble learning approach. Computational Materials Science, 229, p.112432. https://doi.org/10.1016/j.commatsci.2023.112432
  8. Jain, P., Joshi, U., Joshi, A., Patel, V. & Thakor, S., 2024. Comparative analysis of machine learning techniques for predicting wear and friction properties of MWCNT reinforced PMMA nanocomposites. Ain Shams Engineering Journal, 15(9), p.102895. https://doi.org/10.1016/j.asej.2024.102895
  9. Kurt, H.I. & Oduncuoglu, M., 2015. Application of a neural network model for prediction of wear properties of ultrahigh molecular weight polyethylene composites. International Journal of Polymer Science, 2015, pp.1–11. https://doi.org/10.1155/2015/315710
  10. Aliyu, I.K., Azam, M.U., Lawal, D.U. & Samad, M.A., 2019. Optimization of SiC concentration and process parameters for a wear-resistant UHMWPE nanocomposite. Arabian Journal for Science and Engineering, 45(2), pp.849–860. https://doi.org/10.1007/s13369-019-04164-3
  11. Abdellah, M.Y., Fathi, H.I., Abdelhaleem, A.M. & Dewidar, M., 2018. Mechanical properties and wear behavior of a novel composite of acrylonitrile–butadiene–styrene strengthened by short basalt fiber. Journal of Composites Science, 2(2), p.34. https://doi.org/10.3390/jcs2020034
  12. Bulanda, K., Oleksy, M. & Oliwa, R., 2023. Polymer composites based on polycarbonate/acrylonitrile-butadiene-styrene used in rapid prototyping technology. Polymers, 15(6), p.1565. https://doi.org/10.3390/polym15061565
  13. Amena, B.T., Altenbach, H., Tibba, G.S. & Hossain, N., 2022. Investigation of mechanical properties of coffee husk-HDPE-ABS polymer composite using injection-molding method. Journal of Composites Science, 6(12), p.354. https://doi.org/10.3390/jcs6120354
  14. Raza, M.A., Maqsood, M.F., Rehman, Z.U., Westwood, A., Inam, A., Sattar, M.M.S. & Ilyas, M.T., 2020. Thermally reduced graphene oxide-reinforced acrylonitrile butadiene styrene composites developed by combined solution and melt mixing method. Arabian Journal for Science and Engineering, 45, pp.9559–9568. https://doi.org/10.1007/s13369-020-04845-4
  15. Rasana, N., Jayanarayanan, K., Mohan, H.T. & Keller, T., 2021. Static and dynamic mechanical properties of nanosilica and multiwalled carbon nanotube reinforced acrylonitrile butadiene styrene composites: theoretical mechanism of nanofiller reinforcement. Iranian Polymer Journal, 30, pp.1211–1225. https://doi.org/10.1007/s13726-021-00962-5
  16. Triantou, M.I., Stathi, K.I. & Tarantili, P.A., 2019. Thermal, mechanical, and dielectric properties of injection molded graphene nanocomposites based on ABS/PC and ABS/PP blends. Polymer Composites, 40(S2), pp.E1662–E1672. https://doi.org/10.1002/pc.25112
  17. Joynal Abedin, F.N., Hamid, H.A., Alkarkhi, A.F., Amr, S.S.A., Khalil, N.A., Ahmad Yahaya, A.N. & Zulkifli, M., 2021. The effect of graphene oxide and SEBS-g-MAH compatibilizer on mechanical and thermal properties of acrylonitrile-butadiene-styrene/talc composite. Polymers, 13(18), p.3180. https://doi.org/10.3390/polym13183180
  18. Jatti, V.S., Saiyathibrahim, A., Krishnan, R.M. & Balaji, K., 2024. Prediction of tribological behavior of acrylonitrile butadiene styrene polymer matrix composites employing copper powders. SAE International Journal of Materials and Manufacturing, 17(4), pp.365–374. https://doi.org/10.4271/05-17-04-0026
  19. Davim, J.P., 2017. Green Composites: Materials, Manufacturing and Engineering. De Gruyter, Berlin. https://doi.org/10.1515/9783110435788
  20. Kumar, K., Babu, B.S. & Davim, J.P., 2022. Hybrid Composites: Processing, Characterization, and Applications. De Gruyter, Berlin. https://doi.org/10.1515/9783110724684
  21. Vigneshwaran, G., Vijayaraghavan, M., Sivamanikandan, K., Keerthana, K. and Balaji, K., 2017. Fluid-structure interaction over an aircraft wing. Bd, 13, pp.27-31.
  22. Katkar, A., Balajia, K. and Khandal, S.V., 2022. Numerical studies on fixed wing aircraft aerodynamic performance using injection suction mechanism. International Journal of Vehicle Structures & Systems, 14(5), pp.631-633.
  23. Arunkumar, A., Gowthaman, T.S., Muthuraj, R., Vinothkumar, S. and Balaji, K., 2017. Numerical investigation over dimpled wings of an aircraft. Int. J. Res. Appl. Sci. Eng. Technol, 5, p.2017.
  24. Davim, J.P., 2024. Sustainable and intelligent manufacturing: Perceptions in line with 2030 Agenda of Sustainable Development. BioResources, 19(1). https://doi.org/10.15376/biores.19.1.4-5
  25. Kurien, R.A., Kannan, G., Kurup, G.B., Reji, G.S., Santhosh, A., Paul, D. & Siengchin, S., 2024. Comparative mechanical and morphological characteristics of an innovative hybrid composite of vetiver and jute. Journal of Polymer Research, 31(12), p.356. https://doi.org/10.1007/s10965-024-04208-9
  26. Kurien, R.A., Koshy, C.P., Santhosh, A., Kurup, G.B., Paul, D. & Reji, G.S., 2022. A study on vetiver fiber and lemongrass fiber reinforced composites. Materials Today: Proceedings, 68, pp.2640–2645. https://doi.org/10.1016/j.matpr.2022.09.563
  27. Kurien, R.A., Arshad, A., Joseph, A., Sunil, A., Cherian, B.T., Rangappa, S.M. & Siengchin, S., 2024. Agave-jute fiber–reinforced hybrid composite for lightweight applications: Effect of hybridization. Biomass Conversion and Biorefinery, pp.1–14. https://doi.org/10.1007/s13399-024-05984-6
  28. Kurien, R.A., Kannan, G., Kurup, G.B., Reji, G.S., Santhosh, A., Paul, D. & Siengchin, S., 2024. Comparative mechanical and morphological characteristics of an innovative hybrid composite of vetiver and jute. Journal of Polymer Research, 31(12), p.356. https://doi.org/10.1007/s10965-024-04208-9
  29. Kannan, G., Thangaraju, R., Suttiruengwong, S., Shanmugam, V., Rangappa, S.M., Sumesh, K.R. & Siengchin, S., 2024. Effect of drilling process parameters on agro-waste-based polymer composites reinforced with banana fiber and coconut shell filler. Biomass Conversion and Biorefinery, pp.1–14. https://doi.org/10.1007/s13399-024-06140-w
  30. Kurien, R.A., Selvaraj, D.P., Sekar, M., Preno, C., Praveen, K.M. & Koshy, 2020. Tribological and mechanical performance characteristics of epoxy-resin composites reinforced with multi-walled carbon nanotubes for sustainable applications. Journal of Green Engineering, 10, pp.8859–8873.
  31. Balaji, K., Babu, V. and Sulthan, S., 2022. Design and development of multipurpose agriculture drone using lightweight materials. SAE International Journal of Aerospace, 16(01-16-02-0012), pp.177-183. https://doi.org/10.4271/01-16-02-0012
  32. Chen, K. & Liu, L., 2011. Geometric data perturbation for privacy preserving outsourced data mining. Knowledge and Information Systems, 29, pp.657–695. https://doi.org/10.1007/s10115-010-0362-4
  33. Ghouchan Nezhad Noor Nia, R., Jalali, M. & Houshmand, M., 2022. A graph-based k-nearest neighbor (KNN) approach for predicting phases in high-entropy alloys. Applied Sciences, 12(16), p.8021. https://doi.org/10.3390/app12168021
  34. Champa-Bujaico, E., García-Díaz, P. & Díez-Pascual, A.M., 2022. Machine learning for property prediction and optimization of polymeric nanocomposites: A state-of-the-art. International Journal of Molecular Sciences, 23(18), p.10712. https://doi.org/10.3390/ijms231810712
  35. Kadri, K., Kallel, A., Guerard, G., Abdallah, A.B., Ballut, S., Fitoussi, J. & Shirinbayan, M., 2024. Study of composite polymer degradation for high pressure hydrogen vessel by machine learning approach. Energy Storage, 6(4), e645. https://doi.org/10.1002/est2.645
  36. Rong, M.Z., Zhang, M.Q. & Ruan, W.H., 2006. Surface modification of nanoscale fillers for improving properties of polymer nanocomposites: A review. Materials Science and Technology, 22(7), pp.787–796. https://doi.org/10.1179/174328406x101247
  37. Zhang, X., Shi, C., Liu, E., Zhao, N. & He, C., 2018. Effect of interface structure on the mechanical properties of graphene nanosheets reinforced copper matrix composites. ACS Applied Materials & Interfaces, 10(43), pp.37586–37601. https://doi.org/10.1021/acsami.8b09799
  38. Vijayan, S.N., Chelladurai, S.J.S., Saiyathibrahim, A., Rakesh, A.J.I.J., Thriveni, K., Preethi, V., Jatti, V.S., Karthik, S., Balaji, K. & Saranya, S., 2024. Static analysis of aluminum alloy ingot/zirconium diboride composites for automotive applications. SAE International Journal of Materials and Manufacturing, 18(1). https://doi.org/10.4271/05-18-01-0007
  39. Biswal, M., Jada, N., Mohanty, S. & Nayak, S.K., 2015. Recovery and utilisation of non-metallic fraction from waste printed circuit boards in polypropylene composites. Plastics Rubber and Composites Macromolecular Engineering, 44(8), pp.314–321. https://doi.org/10.1179/1743289815y.0000000021
  40. Vu, H.L., Ng, K.T.W., Richter, A. & An, C., 2022. Analysis of input set characteristics and variances on k-fold cross validation for a recurrent neural network model on waste disposal rate estimation. Journal of Environmental Management, 311, p.114869. https://doi.org/10.1016/j.jenvman.2022.114869
  41. Sawant, D.A., Jatti, V.S., Vibhute, A., Saiyathibrahim, A., Murali Krishnan, R., Bembde, S. & Balaji, K., 2024. Prediction of burn rate of ammonium perchlorate–hydroxyl-terminated polybutadiene composite solid propellant using supervised regression machine learning algorithms. Aerospace Systems, pp.1–9. https://doi.org/10.1007/s42401-024-00305-1
  42. Wiens, T.S., Dale, B.C., Boyce, M.S. & Kershaw, G.P., 2007. Three way k-fold cross-validation of resource selection functions. Ecological Modelling, 212(3–4), pp.244–255. https://doi.org/10.1016/j.ecolmodel.2007.10.005
  43. Jia, W., Sun, M., Lian, J. & Hou, S., 2022. Feature dimensionality reduction: A review. Complex & Intelligent Systems, 8(3), pp.2663–2693. https://doi.org/10.1007/s40747-021-00637-x
  44. Everson, R.M. & Fieldsend, J.E., 2005. Multi-class ROC analysis from a multi-objective optimisation perspective. Pattern Recognition Letters, 27(8), pp.918–927. https://doi.org/10.1016/j.patrec.2005.10.016
  45. Moradi, R., Berangi, R. & Minaei, B., 2019. A survey of regularization strategies for deep models. Artificial Intelligence Review, 53(6), pp.3947–3986. https://doi.org/10.1007/s10462-019-09784-7
  46. Banerjee, P., Dehnbostel, F.O. & Preissner, R., 2018. Prediction is a balancing act: Importance of sampling methods to balance sensitivity and specificity of predictive models based on imbalanced chemical data sets. Frontiers in Chemistry, 6. https://doi.org/10.3389/fchem.2018.00362
  47. Tharwat, A., Mahdi, H., Elhoseny, M. & Hassanien, A.E., 2018. Recognizing human activity in mobile crowdsensing environment using optimized K-NN algorithm. Expert Systems with Applications, 107, pp.32–44. https://doi.org/10.1016/j.eswa.2018.04.017
  48. Aljrees, T., 2024. Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning. PLoS ONE, 19(1), e0295632. https://doi.org/10.1371/journal.pone.0295632
  49. Chen, K. et al., 2019. Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. Water Research, 171, p.115454. https://doi.org/10.1016/j.watres.2019.115454
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