Intelligent Vacuum Thermoforming Process
This addresses quality control challenges for manufacturers in vacuum thermoforming, but it is incremental as it applies an existing method to a new domain.
The research tackled inconsistent quality in vacuum thermoforming by developing a vision-based system to predict and optimize process parameters, resulting in reduced defects and improved production efficiency.
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.