Ensuring consistent quality in vacuum thermoforming presents challenges due to variations in material properties and tooling configurations. This research introduces a vision-based quality control system to predict and optimise process parameters, thereby enhancing part quality with minimal data requirements. A comprehensive dataset was developed using visual data from vacuum-formed samples subjected to various process parameters, supplemented by image augmentation techniques to improve model training. A k-Nearest Neighbour algorithm was subsequently employed to identify adjustments needed in process parameters by mapping low-quality parts to their high-quality counterparts. The model exhibited strong performance in adjusting heating power, heating time, and vacuum time to reduce defects and improve production efficiency.
@article{arxiv.2509.13250,
title = {Intelligent Vacuum Thermoforming Process},
author = {Andi Kuswoyo and Christos Margadji and Sebastian W. Pattinson},
journal= {arXiv preprint arXiv:2509.13250},
year = {2025}
}
Comments
Contains 6 figures in total, 15 pages. Under revision for Journal of Intelligent Manufacturing