# Intelligent Vacuum Thermoforming Process

arXiv: https://arxiv.org/abs/2509.13250 · HTML: https://mlsanity.com/paper/2509.13250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Vacuum-formed 3D printed electronics: fabrication of thin, rigid and free-form interactive surfaces](https://mlsanity.com/paper/2104.12601/) (2021)
- [Machine learning for automated quality control in injection moulding manufacturing](https://mlsanity.com/paper/2206.15285/) (2022)

## Abstract (arXiv)

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.