Predmnijevanje napona naprezanja kod vrućeg sabijanja čelika s CAE NN i hiperboličnom - sinusoidnom jednadžbom

Hot compression experiments are carried out on steel workpieces by means of Gleeble 1500 thermo mechanical simulator in wide range of temperatures 800 °C - 1200 °C with strain rates 0,1 s-1, 1,0 s-1 and 8,0 s-1and true strains of 0,0 to 0,5. Hot flow curves were estimated by means of the CAE neural networks. The methods of constant smoothness parameter and non-constant (ellipsoidal) smoothness parameter were applied. The use of the latter proved more exact (up to 3,4 %) and simpler if we compare it with the existing data for the flow curve prediction of tool steel by BP NN (up to 7 %), as the proposed method yields better results. The activation energy and other parameters in hyperbolic-sine equation were calculated according to the method proposed by McQueen et al. and according to the method recently proposed by Kugler et al. The latter yields better results at predicting the maximum values of hot flow curves.Pomoću termomehaničkog simulatora Gleeble 1500 izvedeni su vrući pokusi sabijanja čeličnih proba u temperaturnom rasponu 800 °C - 1200 °C, brzinom deformacije 0,1 s-1, 1,0 s-1 i 8,0 s-1 i stupnja defor-macije od 0,0 do 0,5. Naprezanja materijala određena su pomoću CAE neuralnih mreža. Rabljene su metode stalnog i nestalnog (elipsoidnog) parametra glatkoće. Upotreba zadnjih pokazala se za točniju (do 3,4 %) i jednostavniju ako ih se usporedi s znanima podacima krivulje naprezanja alatnog čelika metodom BP NN (do 7 %). Aktivacijska metoda i ostali parametri u hiperbolično - sinusoidnoj jednadžbi izračunani su metodom koju predlaže McQueen i ostali te novijom metodom predloženoj od Kuglera i ostalih. Ta zadnja ima bolje rezultate za predmnijevanje maksimalnih vrijednosti krivulja tečenja u vrućem

Modeliranje i pouzdanost izračunatih krivulja tečenja

Flow curves are very important input data for numerical modelling of industrial processes and for direct industrial applications. Precise thermal and mechanical testing of low carbon silicon steel showed obvious differences in yield stresses according to permissible oscillations of chemical composition. Since conventional Hajduk, Elfmark and Spittel equations for flow curve calculation are very rigid and cannot describe the local changes of yield stresses caused by phase transformations, a new neural network aproach for modelling the physical phenomena in materials science has been developed. The obtained results showed that neural-network method is a powerful tool, and it can be applied directly in solving problems of materials science (e.g. materials testing support, mathematical simulation of materials forming process).Krivulje tečenja su vrlo važan ulazni podatak za simuliranje industrijskih procesa s numeričkim metodama i za izravnu upotrebu. Precizno termičko i mehaničko testiranje niskougljičnog silicijskog čelika pokazuje velike razlike naprezanja tečenja između istih čelika s različitim kemijskim sastavom u rangu dozvoljenih tolerancija. Obične jednadžbe za opis krivulja tečenja (Hajduk, Spittel, Elfmark) su ograničene i nemaju mogućnost opisa lokalne promujene naprezanja zbog faznih transfor-macija. Da bi se rješilo ovaj problem upotrebljena je nova metoda za opis tih procesa - neuronske mreže. Rezultati takve obrade eksperimentalnih podataka pokazuje na veliku sposobnost tih metoda za opis takvih i sličnih procesa u materijalu (potpora testiranju materijala, matematičko simuliranje deformiranja materijala)

Predviđanje trošenja valjaka za toplo valjanje traka

Wear of work rolls has significant influence on the flatness of hot rolled strip and therefore it is a technological parameter, which should be considered in planning and realization of the rolling technology. The relationship between the rolling program, pass schedule, and the work roll wear profile will be presented with the CAE neural network. The same method is applied in the program for prediction of the optimal shape and prediction of wear progress during the rolling process on one pair of work rolls. This study of the wear of work rolls refers to the Steckel rolling strip technology.Trošenje valjaka ima velik utjecaj na ravnoću toplo valjanih traka i parametar je kojeg treba imati u vidu kod planiranja i realiziranja tehnologije valjanja. U ovom članku prikazana je korelacija između asortimana valjanja, programa valjanja i opisa trošenja profila valjaka pomoću CAE neuronskih mreža. Istom metodom (CAE) služi se za predviđanje optimalnog oblika i razvoja tro-šenja radnih valjaka. Ova studija odnosi se na trošenje valjaka pri vrućem valjanju na Steckel valjačkom stanu

O utjecaju ljudskog faktora na mehanička svojstva u istiskivanju aluminija na toplo

A non-parametric model was proposed for modelling the influence of different technological and chemical parameters on the mechanical properties of the 6082 aluminium alloys during the hot extrusion process with a special consideration of human factor. It was shown that human factor (influence of process engineers) was important and that it could be efficiently modeled and taken into account by the proposed Conditional Average Estimator (CAE) method. Production might be improved (optimized) by a proper education and/or by eliminating critical process engineers. It was found that the highest values for elongation and yield stress did not coincide with the range of the most frequent combinations of input parameters.Predložen je neparametarski model za modeliranje utjecaja različitih tehnoloških i kemijskih parametara na mehanička svojstva aluminijske legure 6082 tijekom istiskivanja na toplo s posebnim razmatranjem na ljudskom faktorom. Pokazano je, da je ljudski faktor (utjecaj proces inženjera) važan i da se može efikasno uzeti u obzir s predloženom CAE metodom. Proizvodnja se može poboljšati (optimirati) uz odgovarajuće obrazovanje i/ili uklanjanje kritičnih proces inženjera. Zapaženo je, da se najviše vrijednosti produljenja i naprezanja tečenja ne podudaraju s područjem najčešćih kombinacija ulaznih parametara

Predmnijevanje napona naprezanja kod vrućeg sabijanja čelika s CAE NN i hiperboličnom - sinusoidnom jednadžbom

Hot compression experiments are carried out on steel workpieces by means of Gleeble 1500 thermo mechanical simulator in wide range of temperatures 800 °C - 1200 °C with strain rates 0,1 s-1, 1,0 s-1 and 8,0 s-1and true strains of 0,0 to 0,5. Hot flow curves were estimated by means of the CAE neural networks. The methods of constant smoothness parameter and non-constant (ellipsoidal) smoothness parameter were applied. The use of the latter proved more exact (up to 3,4 %) and simpler if we compare it with the existing data for the flow curve prediction of tool steel by BP NN (up to 7 %), as the proposed method yields better results. The activation energy and other parameters in hyperbolic-sine equation were calculated according to the method proposed by McQueen et al. and according to the method recently proposed by Kugler et al. The latter yields better results at predicting the maximum values of hot flow curves.Pomoću termomehaničkog simulatora Gleeble 1500 izvedeni su vrući pokusi sabijanja čeličnih proba u temperaturnom rasponu 800 °C - 1200 °C, brzinom deformacije 0,1 s-1, 1,0 s-1 i 8,0 s-1 i stupnja defor-macije od 0,0 do 0,5. Naprezanja materijala određena su pomoću CAE neuralnih mreža. Rabljene su metode stalnog i nestalnog (elipsoidnog) parametra glatkoće. Upotreba zadnjih pokazala se za točniju (do 3,4 %) i jednostavniju ako ih se usporedi s znanima podacima krivulje naprezanja alatnog čelika metodom BP NN (do 7 %). Aktivacijska metoda i ostali parametri u hiperbolično - sinusoidnoj jednadžbi izračunani su metodom koju predlaže McQueen i ostali te novijom metodom predloženoj od Kuglera i ostalih. Ta zadnja ima bolje rezultate za predmnijevanje maksimalnih vrijednosti krivulja tečenja u vrućem

Modeliranje i pouzdanost izračunatih krivulja tečenja

Flow curves are very important input data for numerical modelling of industrial processes and for direct industrial applications. Precise thermal and mechanical testing of low carbon silicon steel showed obvious differences in yield stresses according to permissible oscillations of chemical composition. Since conventional Hajduk, Elfmark and Spittel equations for flow curve calculation are very rigid and cannot describe the local changes of yield stresses caused by phase transformations, a new neural network aproach for modelling the physical phenomena in materials science has been developed. The obtained results showed that neural-network method is a powerful tool, and it can be applied directly in solving problems of materials science (e.g. materials testing support, mathematical simulation of materials forming process).Krivulje tečenja su vrlo važan ulazni podatak za simuliranje industrijskih procesa s numeričkim metodama i za izravnu upotrebu. Precizno termičko i mehaničko testiranje niskougljičnog silicijskog čelika pokazuje velike razlike naprezanja tečenja između istih čelika s različitim kemijskim sastavom u rangu dozvoljenih tolerancija. Obične jednadžbe za opis krivulja tečenja (Hajduk, Spittel, Elfmark) su ograničene i nemaju mogućnost opisa lokalne promujene naprezanja zbog faznih transfor-macija. Da bi se rješilo ovaj problem upotrebljena je nova metoda za opis tih procesa - neuronske mreže. Rezultati takve obrade eksperimentalnih podataka pokazuje na veliku sposobnost tih metoda za opis takvih i sličnih procesa u materijalu (potpora testiranju materijala, matematičko simuliranje deformiranja materijala)

Primjena genetskog programiranja u CAE neuronskih mreža za prognozu izdržljivosti kod savijanja ZnTiCu traka

Genetic programming (GP) and CAE NN analysis have been applied for the prediction of bending capability of rolled ZnTiCu alloy sheet. Investigation revealed that an analysis with CAE NN is faster than GP but less accurate for lower amount of data. Both methods enable good assessment of separate influencing parameters in the complex system.Metode genetskog programiranja (GP) i CAE NN bile su upotrebljene za studij utjecaja izdržljivosti savijanja tankih ploča slitine ZnTiCu. Istraživanje je pokazalo da je CAE NN metoda brža od GP metode a istovremeno je manje precizna pri manjoj bazi podataka. Obje primenjene metode dobro vrednuju utjecaj parcijalnih komponenata kompleksnog sistema

Hot forming of AISI A2 tool steel

For further increase of economy of production of AISI A2 tool steel a study of possibility of expanding the hot working range and better prediction of flow stress has been carried out. By employing hot compression tests it was proved, that initial microstructures have influence on the lower limit and chemical composition on upper limit of hot working range. A CAE Neural Networks was applied to predict the flow stresses for intermediate values of strain rates and temperatures. For optimization purposes the activation energies and constants of the hyperbolic sine function for two temperatures ranges (850-1000°C and 1000-1150°C) were calculated

Genetic programming and cae neural networks approach for prediction of the bending capability of ZnTiCu sheets

Genetic programming (GP) and CAE NN analysis have been applied for the prediction of bending capability of rolled ZnTiCu alloy sheet. Investigation revealed that an analysis with CAE NN is faster than GP but less accurate for lower amount of data. Both methods enable good assessment of separate influencing parameters in the complex system