Research on GFRTP Thermoforming Process based on Microstructure Analysis

Abstract

This study aims to optimize the thermoforming process of Glass Fiber Reinforced Thermoplastics (GFRTP) by investigating the effects of temperature, pressure, stacking angle, and heating time. Systematic adjustments of these parameters enable a detailed microstructural analysis using optical microscopy. Python-based image analysis is employed to extract key quantitative features, such as void fraction, to support process optimization. Furthermore, an Artificial Neural Network (ANN) model is developed to predict optimal processing conditions. The ANN results identify conditions that minimize void fraction, demonstrating the effectiveness of the proposed optimization approach. This study provides a theoretical foundation for GFRTP manufacturing and introduces an innovative combination of image analysis and ANN modeling to enhance production efficiency and product quality, promoting broader composite applications.