Deep Learning-Based Control Optimization for Glass Bottle Forming
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914105171902464 |
|---|---|
| author | Pujatti, Mattia Di Luca, Andrea Peghini, Nicola Monegaglia, Federico Cristoforetti, Marco |
| author_facet | Pujatti, Mattia Di Luca, Andrea Peghini, Nicola Monegaglia, Federico Cristoforetti, Marco |
| contents | In glass bottle manufacturing, precise control of forming machines is critical for ensuring quality and minimizing defects. This study presents a deep learning-based control algorithm designed to optimize the forming process in real production environments. Using real operational data from active manufacturing plants, our neural network predicts the effects of parameter changes based on the current production setup. Through a specifically designed inversion mechanism, the algorithm identifies the optimal machine settings required to achieve the desired glass gob characteristics. Experimental results on historical datasets from multiple production lines show that the proposed method yields promising outcomes, suggesting potential for enhanced process stability, reduced waste, and improved product consistency. These results highlight the potential of deep learning to process control in glass manufacturing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18412 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning-Based Control Optimization for Glass Bottle Forming Pujatti, Mattia Di Luca, Andrea Peghini, Nicola Monegaglia, Federico Cristoforetti, Marco Artificial Intelligence In glass bottle manufacturing, precise control of forming machines is critical for ensuring quality and minimizing defects. This study presents a deep learning-based control algorithm designed to optimize the forming process in real production environments. Using real operational data from active manufacturing plants, our neural network predicts the effects of parameter changes based on the current production setup. Through a specifically designed inversion mechanism, the algorithm identifies the optimal machine settings required to achieve the desired glass gob characteristics. Experimental results on historical datasets from multiple production lines show that the proposed method yields promising outcomes, suggesting potential for enhanced process stability, reduced waste, and improved product consistency. These results highlight the potential of deep learning to process control in glass manufacturing. |
| title | Deep Learning-Based Control Optimization for Glass Bottle Forming |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.18412 |