Predicting and Improving the Mechanical Strength of a Modified Natural Fiber/TiO2 Polymer Nanocomposite Using Artificial Intelligence
J. Environ. Nanotechnol., Volume 14, No 4 (December 2025) pp. 9-26
Abstract
Artificial Intelligence (AI) including methods such as artificial neural networks (ANN) and machine learning has been employed to address numerous technical challenges. This study employed titanium dioxide (TiO2) and natural fibers derived from Paederia foetida (PF) to fabricate a high-performance hybrid nanocomposite reinforced for structural functions. A chemical mixture of KMnO4 in acetone was utilized to modify the fibers, enhancing the internal properties and improving bonding with the PF/polymer matrix. To enhance the tensile strength (TS) and flexural strength (FS) of the synthesized PF/TiO2 hybrid nanocomposite (PFMNC), an ANN model featuring hyper-parameter enhancement within a linear neural network of 3-5-1 was employed, incorporating 5 neurons in the internal layer, with the application of Box-Behnken Design (BBD). Scanning electron microscopic images indicate that KMnO4 alteration influenced the mechanical strength of the composite. The mechanical assessment findings indicate that variables influenced the tensile strength (TS) and flexural strength (FS) of PFMNC, as demonstrated by the analysis of variance R² = 0.9525 and R² = 0.9526, respectively. The anticipated outcomes closely resembled the experimental results. The model forecasted an ideal TS of 47.5613 MPa and FS of 38.12 MPa. To validate the empirical experimental research, TS and FS analyses were conducted at the projected optimal settings. The TS data indicated an average strength of 40.67 MPa, while the FS exhibited an average value of 52.98 MPa. This work has shown that the ANN-BBD modeling technique effectively attains suitable mechanical property values rapidly, reduce production costs, and conserves resources.
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Reference
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