Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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| URI | http://purl.tuc.gr/dl/dias/47860AFF-3BD3-44C8-8F1E-F2A69147CABC | - |
| Αναγνωριστικό | https://doi.org/10.1007/s10846-010-9427-6 | - |
| Γλώσσα | en | - |
| Μέγεθος | 17 pages | en |
| Τίτλος | Predictive modeling of the Ti6Al4V alloy surface roughness | en |
| Δημιουργός | Tsourveloudis Nikolaos | en |
| Δημιουργός | Τσουρβελουδης Νικολαος | el |
| Εκδότης | Springer Verlag | en |
| Περίληψη | The prediction of surface roughness is important for all materials that undergo manufacturing processes. The Ti6Al4V titanium alloy is commonly used in aerospace, automotive and power generation industries but also in the manufacturing of medical implants, mainly because of its biocompatibility. Here we study the relationship of Ti6Al4V’s surface roughness with critical machining parameters and conditions based on experimental input (machining parameters)-output (surface roughness) data derived during the turning operation. The experimental findings are converted into polynomial models through the Response Surface Methodology (RSM) and into a fuzzy logic system through the Adaptive Neuro-Fuzzy Inference System (ANFIS). The ability of these two methodologies to predict Ti6Al4V’s surface roughness when machined is presented and compared. It is observed that the ANFIS predicts surface roughness with less error mainly when the data used for evaluation are not completely different to those used for training. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-10-07 | - |
| Ημερομηνία Δημοσίευσης | 2010 | - |
| Βιβλιογραφική Αναφορά | N. C. Tsourveloudis , “Predictive modeling of the Ti6Al4V alloy surface roughness”, Intern. J. of Intell. and Robotic Syst.,vol. 60,no.3-4,pp. 513-530,2010.doi :10.1007/s10846-010-9427-6 | en |
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