Wyniki wyszukiwania - Biblioteka Nauki

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Obosnovanie strukturnykh sostavljajushhikh vibrokonvejjernojj mashiny dlja poverkhnostnojj uprochnjajushhejj obrabotki rabochikh organov sel'skokhozjajjstvennykh mashin

100%

Palamarchuk I. , Drachishin V. , Palamarchuk V.

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tom 16

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nr 4

2

Numerical model to predict microstructure of the heat treated of steel elements

86%

Domański T. , Bokota A.

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tom Vol. 11, iss. 2 spec.

29--34

EN

In work the presented numerical models of tool steel hardening processes take into account thermal phenomena and phase transformations. Numerical algorithm of thermal phenomena was based on the Finite Elements Methods of the heat transfer equations. In the model of phase transformations, in simulations heating process continuous heating (CHT) was applied, whereas in cooling process continuous cooling (CCT) of the steel at issue. The phase fraction transformed (austenite) during heating and fractions during cooling of ferrite, pearlite or bainite are determined by Johnson-Mehl-Avrami formulas. The nescent fraction of martensite is determined by Koistinen and Marburger formula or modified Koistinen and Marburger formula. In the simulations of hardening was subject the fang lathe of cone (axisymmetrical object) made of tool steel.

PL

Prezentowany w pracy model numeryczny procesu hartowania stali narzędziowej uwzględnia zjawiska cieplne i przemiany fazowe w stanie stałym. Algorytm numeryczny zjawisk cieplnych oparto na rozwiązaniu metodą elementów skończonych, w sformułowaniu Galernika, równania przewodzenia ciepła. W modelu przemian fazowych korzysta się z wykresów ciągłego nagrzewania (CTPa), oraz z wykresów ciągłego chłodzenia (CTPc) rozważanej stali. Ułamek fazy przemienionej (austenit) oraz ułamki ferrytu, perlitu lub bainitu wyznacza się formułami Johnsona-Mehla i Avramiego. Ułamek powstającego martenzytu wyznacza się wzorem Koistinena i Marburgera lub zmodyfikowanym wzorem Koistinena i Marburgera.

3

Wykorzystanie trójcewkowego wzbudnika cylindrycznego w procesie nagrzewania indukcyjnego kół zębatych

72%

Niklewicz M. , Smalcerz A.

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2009

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tom z. 118

179-186

PL

Celem niniejszego artykułu jest przedstawienie wyników analizy numerycznej nagrzewania indukcyjnego kół zębatych w procesie hartowania. Analiza dotyczyła układów ze wzbudnikami cylindrycznymi o różnych konfiguracjach (wzbudnik o jednej i trzech cewkach). Przeprowadzone badania obejmowały nagrzewanie jedno-częstotliwościowe i dwuczęstotliwościowe - sekwencyjne. Ponadto została przeprowadzona analiza wpływu parametrów procesu, tj. natężenia i częstotliwości prądu wzbudników na rozkład temperatury kół zębatych.

EN

The paper concerns induction heating numerical analysis in hardening process of gears. Various configurations of inductor including one- and three- coil inductor have been analyzed. Calculations have concerned two types of heating, with use of one- and two frequencies of inductor current (sequentially). Additionally analysis of basic process parameters on gear temperature distribution have been carried out and results are presented.

4

Modelling of quenching process of medium-carbon steel

72%

Kulawik A. , Bokota A.

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tom Vol. 10, iss. 1

83-88

EN

The paper refers to numerical modelling of the hardening process of steel C45. In the algorithm the heat transfer equation, equilibrium equations and macroscopic model of phase transformations, basis of CCT diagrams, are used. Coupling between basic phenomena of hardening process is considered, in particular the influence of latent heat on the fields of temperature, and also thermal, structural and plastic strains and transformation - induced plasticity in the model is taken into account as well. The method of calculating the phase transformation during heating applied by the authors uses data from the continuous heating diagram (CHT). The homogenization line of austenite determines the end of heating. The influence of austenisation temperature on the kinetics of transformations is taken into account. To calculate the increase of martensite content Koistinen-Marburger formula is used. Field of stresses and strains are obtained from solutions of finite element method equations of equilibrium in increment form.

5

Właściwości fazy cynkowej oraz jej wpływ na twardnienie cementu glinowego

72%

Bochenek A.

Cement Wapno Beton

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2013

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tom R. 18/80, nr 6

370--377

PL

Zbadano właściwości wiążące fazy Ca3Zn3Al4O18 uzyskanej w dużym, okresowym piecu laboratoryjnym. Wykazała ona stosunkowo szybkie wiązanie i znaczną wytrzymałość po 2 dniach twardnienia, jednak jej przyrost w okresie do 28 dni był mały. Było to spowodowane wolną hydratacją fazy cynkowej w tym okresie. Dodatek około 11% fazy cynkowej do cementu glinowego spowodował przyspieszenie wiązania, lecz równocześnie znaczne zmniejszenie wytrzymałości, w całym badanym okresie, to znaczy do 28 dni. Jest to także spowodowane zmniejszeniem szybkości hydratacji cementu glinowego.

EN

The binding properties of Ca3Zn3Al4O18 phase, synthesized in large periodical laboratory furnace, was examined It has shown quick set and high strength after two days of hardening, however, its development in the period of 28 days was rather low. It was the effect of the slow hydration of zinc phase in this period. The addition of about 11% of zinc phase to calcium aluminate cement caused the set acceleration and simultaneously significant strength decrease during the study period i.e. till 28 days. It is caused by the lowering of hydration rate of calcium aluminate cement.

6

Influence of the Reclaim Addition into the Matrix on the Hardening Process Kinetics of the Moulding Sand with Furfuryl Resin

72%

Jamrozowicz Ł. , Kolczyk J. , Wojtuń P.

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tom Vol. 18, iss. 1

41--46

EN

Measurements of the hardening process course of the selected self-hardening moulding sands with the reclaimed material additions to the matrix, are presented in the hereby paper. Moulding sands were produced on the „Szczakowa” sand (of the Sibelco Company) as the matrix of the main fraction FG 0,40/0,32/0,20, while the reclaim was added to it in amounts of 20, 50 and 70%. Regeneration was performed with a horizontal mechanical regenerator capacity of 10 t/h. In addition, two moulding sands, one on the fresh sand matrix another on the reclaimed matrix, were prepared for comparison. Highly-fluid urea-furfuryl resin was used as a binder, while paratoluensulphonic acid as a hardener. During investigations the hardening process course was determined, it means the wave velocity change in time: cL = f(t). The hardening process kinetics was also assessed (dClx/dt = f(t)). Investigations were carried out on the research stand for ultrasound tests. In addition strength tests were performed.

7

Influence of modelling phase transformations with the use of LSTM network on the accuracy of computations of residual stresses for the hardening process

58%

Wróbel J. , Kulawik A.

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tom Vol. 71, nr 4

art. no. e145681

EN

Replacing mathematical models with artificial intelligence tools can play an important role in numerical models. This paper analyses the modeling of the hardening process in terms of temperature, phase transformations in the solid state and stresses in the elastic-plastic range. Currently, the use of artificial intelligence tools is increasing, both to make greater generalizations and to reduce possible errors in the numerical simulation process. It is possible to replace the mathematical model of phase transformations in the solid state with an artificial neural network (ANN). Such a substitution requires an ANN network that converts time series (temperature curves) into shares of phase transformations with a small training error. With an insufficient training level of the network, significant differences in stress values will occur due to the existing couplings. Long-Short-Term Memory (LSTM) networks were chosen for the analysis. The paper compares the differences in stress levels with two coupled models using a macroscopic model based on CCT diagram analysis and using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) and Koistinen-Marburger (KM) equations, against the model memorized by the LSTM network. In addition, two levels of network training accuracy were also compared. Considering the results obtained from the model based on LSTM networks, it can be concluded that it is possible to effectively replace the classical model in modeling the phenomena of the heat treatment process.