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.
Abstract (translated)
在真空热成型中保证一致的质量面临挑战，因为材料特性和模具配置的变化会导致质量问题。这项研究引入了一种基于视觉的质量控制系统，旨在预测和优化工艺参数，从而以最少的数据需求提高部件质量。通过收集不同工艺参数下真空成型样品的视觉数据，并辅以图像增强技术来改进模型训练，开发了一个全面的数据集。随后采用k-近邻算法识别需要调整的工艺参数，通过将低质量零件映射到高质量零件上来确定这些调整。 该模型在调节加热功率、加热时间和抽真空时间方面表现出了强大的性能，能够减少缺陷并提高生产效率。
URL
https://arxiv.org/abs/2509.13250
