Machine learning for screw design in single-screw extrusion
Artykuł w czasopiśmie
MNiSW
70
Lista 2024
| Status: | |
| Autorzy: | Polychronopoulos Nickolas D., Moustris Konstantinos P., Karakasidis Theodoros, Sikora Janusz, Krasinskyi Volodymyr, Sarris Ioannis E., Vlachopoulos John |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 5 |
| Wolumen/Tom: | 65 |
| Strony: | 2607 - 2623 |
| Impact Factor: | 3,2 |
| Web of Science® Times Cited: | 7 |
| Scopus® Cytowania: | 7 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | This research was funded by the European Union's programme for research and innovation Horizon Europe under the Marie Skłodowska-Curie Action grant agreement No. 101129698-PROMATAI-Horizon-MSCA-2022-SE-01. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| Artificial intelligence (AI) methods have significantly impacted various areas of technology, particularly in fields where large datasets are available. Screw designs are proprietary, and there is very limited information available in the open literature. In this study, we generated a dataset of 232 designs using com- puter simulation software for screw extrusion, involving solids transport, melt- ing, and melt pumping. The parameters (features) and the outputs (targets) were introduced into four powerful machine learning (ML) algorithms. The capabilities of the four algorithms were assessed by comparing the predictions of each of the algorithms to the corresponding results of the simulations. Three of the algorithms demonstrated satisfactory performance, with the best- performing one being further tested on an “unseen” dataset, which involved a screw of 75 mm and another of 127 mm in diameter. A machine-learning tech- nique called Permutation Feature Importance (PFI) was used to identify the features (parameters) with the greatest impact on the predictions. It is sug- gested that the same ML methodologies could be applied to datasets of existing real screw designs. |
