Journal of Materials Exploration and Findings (JMEF)


Polylactic acid (PLA) material has the potential to be applied in various industrial fields, but this material has shortcomings in terms of mechanical properties, especially mechanical strength, due to brittleness nature of PLA. The manufacture of PLA composite material with the addition of natural fibers as a reinforcing phase is one of the methods to increase the impact strength and maintain the biodegradable properties of the material. However, in theory, there are many factors that affect the mechanical properties of composite materials, thus making the mechanical properties of composites more complex than monolithic materials. The mechanical properties of these composite materials can be predicted using deep learning by paying attention to the relationship between factors, and between factors and their mechanical properties. This relationship has an important role in creating a predictive model with good accuracy. Therefore, correlation analysis is an important thing to do. Correlation analysis was applied using Python programming language to determine the relationship between the impact strength of natural fiber-reinforced PLA biocomposites with its feature information: chemical composition, density, dimensions, surface chemical treatment of natural fibers, matrix-reinforcement volume fraction, and the type of processing used to manufacture the material.


  1. Hopewell, J., Dvorak, R., & Kosior, E. (2009). Plastics recycling: challenges and opportunities. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1526), 2115-2126.
  2. Kumar, K. S., Van Swygenhoven, H., & Suresh, S. (2003). Mechanical behavior of nanocrystalline metals and alloys. Acta materialia, 51(19), 5743-5774.
  3. Fatriansyah, J. F., Surip, S. N., Jaafar, W. N. R. W., Phasa, A., Uyup, M. K. A., & Suhariadi, I. (2022). Isothermal crystallization kinetics and mechanical properties of PLA/Kenaf biocomposite: Comparison between alkaline treated kenaf core and bast reinforcement. Materials Letters, 319, 132294.
  4. Rahmat, N. G., Fatriansyah, J. F., Dhaneswara, D., & Kaban, A. P. S. (2020). Study of Zeolite Usage in Thermal Degradation Process of Polypropylene Pyrolysis. In Materials Science Forum (Vol. 1000, pp. 331-336). Trans Tech Publications Ltd.
  5. Rashdan, A., Sakinah, Hazlina, and Iqbal, M. (2016). Performance of Polylactic Acid Natural Fiber Biocomposite. International Journal of Applied Chemistry, 12(1), 72-77.
  6. Rasal, R. M., Janorkar, A. V., and Hirt, D. E. (2010). Poly(lactic acid) modifications. Progress in Polymer Science, 35, 338-356.
  7. Fatriansyah, J. F., Surip, S. N., and Hartoyo, F. (2022). Mechanical Property Prediction of Poly(Lactic Acid) Blends Using Deep Neural Network. Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy, 9(1), 141-144.
  8. Rahman, A., Deshpande, P., Radue, M. S., Odegard, G. M., Gowtham, S., Ghosh, S., & Spear, A. D. (2021). A machine learning framework for predicting the shear strength of carbon nanotube-polymer interfaces based on molecular dynamics simulation data. Composites Science and Technology, 207, 108627.
  9. Fathi, H., Nasir, V., & Kazemirad, S. (2020). Prediction of the mechanical properties of wood using guided wave propagation and machine learning. Construction and Building Materials, 262, 120848.
  10. Rong, Q., Wei, H., Huang, X., & Bao, H. (2019). Predicting the effective thermal conductivity of composites from cross sections images using deep learning methods. Composites Science and Technology, 184, 107861.
  11. Arunika, A., Fatriansyah, J. F., and Ramadheena, V. A. (2022). Detection of Asphalt Pavement Segregation Using Machine Learning Linear and Quadratic Discriminant Analyses. Evergreen, 9(1), 213-218.
  12. Senthil Muthu Kumar, T., Senthilkumar, K., Chandrasekar, M., Subramaniam, S., Mavinkere Rangappa, S., Siengchin, S., & Rajini, N. (2020). Influence of fillers on the thermal and mechanical properties of biocomposites: an overview. Biofibers and Biopolymers for Biocomposites, 111-133.
  13. Muthuraj, R., Misra, M., Defersha, F., & Mohanty, A. K. (2016). Influence of processing parameters on the impact strength of biocomposites: A statistical approach. Composites Part A: Applied Science and Manufacturing, 83, 120-129.
  14. Sands, D. E. (1977). Correlation and Covariance. Journal of Chemical Education, 54(2), 90.
  15. Schober, P., Boer, C., & Schwarte, L. (2018). Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.



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