518 KB33 KB200020242014Eldem, CengizSahin, IsmailTolga Ozkan, Muratštevilka:1letnik:48str. 81-90COBISSID:1032106ISSN:1580-2949URN:URN:NBN:SI:doc-SDPA02CGenInštitut za kovinske materiale in tehnologijeMateriali in tehnologijeartificial neural networkfaktor zareznega učinkagrednotch-sensitivity factorshaftsstatistical analysisstatistična analizatorsiontorzijaumetna nevronska mrežaDetermination of the notch factor for shafts under torsional stress with artificial neural networks| Uporaba umetnih nevronskih mrež za določanje faktorja zareznega učinka na gredeh, obremenjenih s torzijskimi napetostmi|When designing machine equipment, geometrical figures or discontinuities such as notches, holes, steps and curves can occur. Sudden cross-section changes, discontinuities and force flows cause concentrations, particularly in the stress area. Stress concentrations may be formed due to dimensional features of a material or directions of applied forces. Such stress concentrations are considered as they have a notch effect on the material. The notch effect may lead to a breaking and distortion of a material. In this study, a mathematical model estimating the notch-factor values for a grooved round bar in torsion, a round shaft with a transverse hole in torsion and a round shaft with a shoulder fillet in torsion, using artificial neural networks (ANN) is introduced. The model estimates the notch factor using shaft dimensions, torque and corner rounding values. The ANN model developed in the study quickly and accurately estimates the notch-factor values, otherwise obtained from the catalogues with complicated analytical calculations. In this model, the uncertainties occurring in analytical calculations and the calculation errors were eliminated, thus long calculation times were saved as well. The results reviewing the performance of the ANN model developed for a grooved round bar in torsion, a round shaft with a transverse hole in torsion and a round shaft with a shoulder fillet in torsion were quite good. In the study, amultiple regression analysis of the data was also performed, but no conclusion evaluating the data was obtainedPri konstruiranju strojnih delov se pojavljajo nezvezne geometrijske oblike, kot so zareze, luknje, stopnice in krivine. Nenadna sprememba prereza, nezveznosti in potek sil povzročajo koncentracijo napetosti v napetostnem območju. Koncentracije napetosti v materialu lahko nastanejo zaradi dimenzijskih sprememb ali sprememb smeri delovanja sil. Taka koncentracija napetosti se obravnava kot zarezni učinek v materialu. Zarezni učinek lahko povzroči porušitev ali izkrivljenje materiala. V tej študiji je predstavljen matematični model umetne nevronske mreže (ANN), ki lahko obravnava faktor zareznega učinka okrogle palice z utorom, okrogle gredi s prečno odprtino, obremenjeno s torzijo, in okrogle gredi z zaokroženim prehodom. Model določa faktor zareznega učinka z uporabo dimenzij, navora in radija zaokrožitev. Razvit ANN-model omogoča hitrejše in bolj zanesljivo določanje faktorja zareznega učinka, ki ga sicer dobimo iz katalogov z zapletenimi analitičnimi preračunavanji. V tem modelu so odpravljene nezanesljivosti, ki se pojavljajo pri analitskem preračunavanju, odpravljene so računske napake in prihranjeno nam je dolgotrajno preračunavanje. Pregledane so bile zmogljivosti ANN-modela, razvitega za torzijo okrogle palice z utorom, torzijo okrogle gredis prečno odprtino in okrogle gredi z zaobljenim prehodom. V študiji je bila izvedena tudi multipla regresijska analiza podatkov, vendar ni bilo mogoče izluščiti ugotovitve, ki bi prispevala k oceni podatkovTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaInštitut za kovinske materiale in tehnologije