An Innovative AI-Integrated Approach for Identifying the Tensile Robustness of Polymeric Materials

Year : 2025 | Volume : 13 | Issue : 01 | Page : 90 97
    By

    M.V. Kulkarni,

  • P. William,

  • Pravin B Khatkale,

  • Sandip R. Thorat,

  • Santosh Kumar Sharma,

  • Apurv Verma,

  1. Assistant Professor, Department of Engineering Science and Humanities, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
  2. Assistant Professor, Department of Information Technology, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
  3. Assistant Professor, Department of Mechanical Engineering, Sanjivani University, Kopargaon, Maharashtra, India
  4. Assistant Professor, Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
  5. Professor, Department of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology, Durg, Chattisgarh, India
  6. Assistant professor, Department of Computer Science and Engineering, SSIPMT, Raipur, Chhattisgarh, India

Abstract

Polymeric materials have so many applications and character similar to flexibility, robustness and lightweight nature they are essential to a large variety of industries. Though, it is difficult to establish their tensile robustness appropriately, particularly in a variety of environmental situation. Provide a recommended Artificial Intelligence (AI)-integrated method to decide the issues of rapidly ascertaining the tensile robustness of the polymeric material. Using machine learning (ML), this study, predicted and classified the tensile strength of polymeric films with unstable compositions based on processing parameters. The study determined on the two molding processes compression molding and extrusion-blow molding as well as collecting with the initial dataset. Predictive and categorization models were then formed utilizing the Binary Dragonfly fine-tuned Multi-Kernel K-nearest neighbor (BD-MKNN) approach. Experiments relating various factors were created with corresponding parameters. Samples underwent a tensile test and the results showed the tensile strength. The suggested BD-MKNN technique enhanced prediction accuracy along with the reliability and enhanced adaptability to varying environmental circumstances. All things considered, our proposed method offers an effective way forward for improving the characterization and use of polymeric materials in a variety of industrial applications, from consumer electronics coupled with medicinal devices to automotive and aerospace.

Keywords: Tensile robustness, polymeric materials, binary dragonfly fine-tuned multi-kernel K-nearest neighbor (BD-MKNN), artificial intelligence (AI), machine learning (ML), film manufacturing processes

[This article belongs to Journal of Polymer and Composites ]

How to cite this article:
M.V. Kulkarni, P. William, Pravin B Khatkale, Sandip R. Thorat, Santosh Kumar Sharma, Apurv Verma. An Innovative AI-Integrated Approach for Identifying the Tensile Robustness of Polymeric Materials. Journal of Polymer and Composites. 2024; 13(01):90-97.
How to cite this URL:
M.V. Kulkarni, P. William, Pravin B Khatkale, Sandip R. Thorat, Santosh Kumar Sharma, Apurv Verma. An Innovative AI-Integrated Approach for Identifying the Tensile Robustness of Polymeric Materials. Journal of Polymer and Composites. 2024; 13(01):90-97. Available from: https://journals.stmjournals.com/jopc/article=2024/view=193232


References

  1. Lin, J.C., Liatsis, P. and Alexandridis, P., 2023. Flexible and stretchable electrically conductive polymer materials for physical sensing applications. Polymer Reviews, 63(1), pp.67-126.
  2. Jia, L., Wu, J., Zhang, Y., Qu, Y., Jia, B., Chen, Z. and Moss, D.J., 2022. Fabrication Technologies for the On‐Chip Integration of 2D Materials. Small Methods, 6(3), p.2101435.
  3. Birren, T.H., 2021. Experimental analysis of semi-brittle deformation: Implications for deformation dynamics (Doctoral dissertation, Iowa State University).
  4. Pastarnokienė, L., Jonikaitė-Švėgždienė, J., Lapinskaitė, N., Kulbokaitė, R., Bočkuvienė, A., Kochanė, T. and Makuška, R., 2023. The effect of reactive diluents on the curing of epoxy resins and properties of the cured epoxy coatings. Journal of Coatings Technology and Research, pp.1-15.
  5. Deng, B., Wang, X., Jiang, D. and Gong, J., 2020. Description of the statistical variations of the measured strength for brittle ceramics: A comparison between two-parameter Weibull distribution and normal distribution. Processing and Application of Ceramics, 14(4), pp.293-302.
  6. Yang, H., Yang, L., Yang, Z., Shan, Y., Gu, H., Ma, J., Zeng, X., Tian, T., Ma, S. and Wu, Z., 2023. Ultrasonic detection methods for mechanical characterization and damage diagnosis of advanced composite materials: A review. Composite Structures, p.117554.
  7. Awasthy, N., Schlangen, E., Hordijk, D., Šavija, B. and Luković, M., 2023. The role of eigen-stresses on apparent strength and stiffness of normal, high strength, and ultra-high performance fiber reinforced concrete. Developments in the Built Environment, 16, p.100277.
  8. Liu, X., Li, Y., Fang, X., Zhang, Z., Li, S. and Sun, J., 2022. Healable and recyclable polymeric materials with high mechanical robustness. ACS Materials Letters, 4(4), pp.554-571.
  9. Gong, K., Hou, L. and Wu, P., 2022. Hydrogen‐Bonding Affords Sustainable Plastics with Ultrahigh Robustness and Water‐Assisted Arbitrarily Shape Engineering. Advanced Materials, 34(19), p.2201065.
  10. Feng, X. and Li, G., 2021. Room-temperature self-healable and mechanically robust thermoset polymers for healing delamination and recycling carbon fibers. ACS Applied Materials & Interfaces, 13(44), pp.53099-53110.
  11. Li, Z., Deng, L., Lv, H., Liang, L., Deng, W., Zhang, Y. and Chen, G., 2021. Mechanically robust and flexible films of ionic liquid‐modulated polymer thermoelectric composites. Advanced Functional Materials, 31(42), p.2104836.
  12. Xue, S., Wu, Y., Liu, G., Guo, M., Liu, Y., Zhang, T. and Wang, Z., 2021. Hierarchically reversible crosslinking polymeric hydrogels with highly efficient self-healing, robust mechanical properties, and double-driven shape memory behavior. Journal of Materials Chemistry A, 9(9), pp.5730-5739.
  13. Chen, Y., Mellot, G., van Luijk, D., Creton, C. and Sijbesma, R.P., 2021. Mechanochemical tools for polymer materials. Chemical Society Reviews, 50(6), pp.4100-4140.
  14. Tan, L.J., Zhu, W. and Zhou, K., 2020. Recent progress on polymer materials for additive manufacturing. Advanced Functional Materials, 30(43), p.2003062.
  15. Vahabi, H., Laoutid, F., Mehrpouya, M., Saeb, M.R. and Dubois, P., 2021. Flame retardant polymer materials: An update and the future for 3D printing developments. Materials Science and Engineering: R: Reports, 144, p.100604.
  16. Altarazi, S.A. and Allaf, R.M., 2017. Designing and analyzing a mixture experiment to optimize the mixing proportions of polyvinyl chloride composites. Journal of Applied Statistics, 44(8), pp.1441-1465.
Regular Issue Subscription Original Research
Volume 13
Issue 01
Received 28/04/2024
Accepted 29/07/2024
Published 15/10/2024
Publication Time 170 Days
314

Login


My IP

PlumX Metrics