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arXiv · 2509.13250

Intelligent Vacuum Thermoforming Process

Andi Kuswoyo·Christos Margadji·Sebastian W. Pattinson
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Abstract

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.

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Andi Kuswoyo, Christos Margadji, Sebastian W. Pattinson. 2025-09-16. Intelligent Vacuum Thermoforming Process. https://arxiv.org/abs/2509.13250

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