RT Journal Article
SR Electronic
A1 Xia, Yuping
A1 Luo, Zhe
T1 Study on the Molding Process of Corncob/Chitosan Composites
JF Manufacturing Technology Journal
YR 2025
VO 25
IS 6
SP 794
OP 806
DO 10.21062/mft.2025.081
UL https://journalmt.com/artkey/mft-202506-0006.php
AB In order to address the pollution caused by petroleum-based plastics and increase the added value of agricultural waste, this study aims to develop an environmentally friendly wood composite material using agricultural waste corncob (CC) and biomass material chitosan (CS) as the matrix, and optimise its molding process to improve its physical and mechanical properties. Based on the single-factor test, the relatively optimal process parameters were preliminarily determined as follows: the CS concentration is 1.8%, the pressure is 25 MPa, and the temperature is 135 °C. At this time, the comprehensive properties of the material reach a density of 1.47 g/cm<sup>3</sup>, a hardness of 16.67 kgf/mm<sup>2</sup>, a flexural strength of 42.2 MPa, and an elastic modulus of 7.2 GPa. Furthermore, the response surface experimental design and analysis method was applied to optimize the composition ratio and molding process parameters, and a response surface model with flexural strength, apparent hardness, and density as response values was established. Through the analysis of the Design-Expert software, a quadratic regression equation was obtained, and its determination coefficient R<sup>2</sup> is higher than 0.9, indicating that the model is significant and reliable. The response surface analysis shows that the optimal parameter combination is a CS concentration of 1.7%, a molding pressure of 26 MPa, and a molding temperature of 138 °C. In the verification test, the flexural strength is measured to be 49.419 MPa, the hardness is 16.585 kgf/mm<sup>2</sup>, and the density is 1.507 g/cm<sup>3</sup>, which is highly consistent with the optimized predicted values. The study shows that the response surface method can effectively establish a quantitative relationship model between process parameters and performance indicators, providing a reliable process optimization method and theoretical support for the green preparation of biomass composites.