Work

Analysis and Modeling of Thermogravimetric Curves of Chemically Modified Wheat Straw Filler-Based Biocomposites Using Machine Learning Techniques

Year: 2025

Type: article

Abstract: Thermogravimetric analysis (TGA) is a technique used to investigate the thermal characteristics of materials by observing fluctuations in sample mass with changes in temperature. Amid the increasing worldwide focus on sustainable materials, biocomposites have become popular for their eco-friendly characteristics. Thermal stability is a crucial factor in determining the performance of biocomposites. The present research improved thermal properties by incorporating wheat straw residual filler into an epoxy resin matrix after various chemical treatments of wheat straw fibers, such as alkali (NaOH) or a combination of silane and alkali treatments. Machine learning (ML) analysis performed in WEKA 3.0 was conducted on thermal data derived from the thermogravimetric measurements of the biocomposites. This research took into account several factors, such as filler loading, single or dual chemical treatment, and temperature, to forecast the thermal-degradation behavior during combustion. Sixteen distinct regression models were used to predict the TGA curves. The K-Nearest Neighbor (KNN) classifier outperformed the other 15 models by achieving an R-squared value of 0.9999, indicating remarkable prediction skills. The strong correlation between the experimental data and the anticipated values confirmed the accuracy of the ML computations.

Source: Journal of Composites Science

DOI: 10.3390/jcs9050221

Authors: Firoz Alam Faroque, Adithya Garimella, Sujay Raghavendra Naganna

Institution: Manipal Academy of Higher Education

Language: English

Topic: Natural Fiber Reinforced Composites

Subfield: Polymers and Plastics

Field: Materials Science

Domain: Physical Sciences

SDG: Responsible consumption and production

Open Access status: gold

Estimated APC paid: $2,172