Intelligent Vacuum Thermoforming Process
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915497501523968 |
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| author | Kuswoyo, Andi Margadji, Christos Pattinson, Sebastian W. |
| author_facet | Kuswoyo, Andi Margadji, Christos Pattinson, Sebastian W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13250 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intelligent Vacuum Thermoforming Process Kuswoyo, Andi Margadji, Christos Pattinson, Sebastian W. Computer Vision and Pattern Recognition Machine Learning I.2.10; I.4.9 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. |
| title | Intelligent Vacuum Thermoforming Process |
| topic | Computer Vision and Pattern Recognition Machine Learning I.2.10; I.4.9 |
| url | https://arxiv.org/abs/2509.13250 |