用视觉识别优化真空热成型参数，少数据也能提质量。
Intelligent Vacuum Thermoforming Process
- 基于视觉数据匹配缺陷件与良品参数，自动调参
- 仅用少量样本即有效减少缺陷，提升生产效率
- 适合制造业做智能质检，尤其缺数据场景
真空热成型中材料属性与工装差异导致质量不稳定。本研究构建基于视觉的质量控制体系，通过采集不同工艺参数下成型样品的视觉数据，并结合图像增强技术扩充数据集，训练模型以预测并优化加热功率、加热时间与真空时间。采用k-近邻算法将低质制品映射至对应高质量制品的参数配置，实现工艺参数动态调整。实验表明，该方法能显著降低缺陷率，提升生产效率，且在数据量有限条件下仍具良好表现。
原文摘要 · Abstract (English)
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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