Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Journal of Materials Engineering and Performance
Erschienen in: Journal of Materials Engineering and Performance 6/2015

01.06.2015

Evaluating the Hot Deformation Behavior of a Super-Austenitic Steel Through Microstructural and Neural Network Analysis

verfasst von: A. Mirzaei, A. Zarei-Hanzaki, M. H. Pishbin, A. Imandoust, Sh. Khoddam

Erschienen in: Journal of Materials Engineering and Performance | Ausgabe 6/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

A series of hot compression tests were conducted in the temperature range of 800-1100 °C under the strain rates of 0.001, 0.01, and 0.1 s−1 to assess the flow behavior and microstructure evolution of a super-austenitic stainless steel. The occurrence of dynamic recrystallization has been characterized as the dominant restoration mechanism operating in the investigated range of temperature. This is considered as the main factor affecting the related flow characteristics of the material. To better analyzing the obtained results, an artificial neural network (ANN) model with single hidden layer composed of 20 neurons has been established to simulate the flow behavior of the material. To train the model, a feed-forward back propagation algorithm has been employed. The reliability of the proposed model has been evaluated using standard statistical indices. In addition, the capability of the model has been assessed under the conditions at which the related data were not incorporated in the model. It was found that the developed ANN model employing this algorithm could efficiently track the work hardening and dynamic softening regions of the deforming material.
Vorheriger Artikel Inverse Thermal Analysis of Titanium GTA Welds Using Multiple Constraints
Nächster Artikel Study on the Crack Propagation Behavior of ×80 Pipeline Steel Under AC Application in High pH Solution

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
R. Jones and V. Randle, Sensitisation Behaviour of Grain Boundary Engineered Austenitic Stainless Steel, Mater. Sci. Eng. A, 2010, 527, p 4275–4280CrossRef
2.
A. Imandoust, A. Zarei-Hanzaki, and H.R. Abedi, Low-Temperature Strain-Induced Ferrite Transformation in Twinning-Induced Plasticity Steel, Scr. Mater., 2012, 67, p 995–998CrossRef
3.
D. Özyürek, An Effect of Weld Current and Weld Atmosphere on the Resistance Spot Weldability of 304L Austenitic Stainless Steel, Mater. Des., 2008, 29, p 597–603CrossRef
4.
B. Guo, H. Ji, X. Liu, L. Gao, R. Dong, M. Jin, and Q. Zhang, Research on Flow Stress During Hot Deformation Process and Processing Map for 316LN Austenitic Stainless Steel, J. Mater. Eng. Perform., 2012, 21, p 1455–1461CrossRef
5.
A. Imandoust, A. Zarei-Hanzaki, S. Heshmati-Manesh, S. Moemeni, and P. Changizian, Effects of Ferrite Volume Fraction on the Tensile Deformation Characteristics of Dual Phase Twinning Induced Plasticity Steel, Mater. Des., 2014, 53, p 99–105CrossRef
6.
A. Zambon, P. Ferro, and F. Bonollo, Compositional and Residual Stress Evaluation of CO2 Laser Welded Superaustenitic AISI, 904L Stainless Steel, Mater. Sci. Eng. A, 2006, 424, p 117–127CrossRef
7.
A. Imandoust, A. Zarei-Hanzaki, M. Sabet, and H.R. Abedi, An Analysis of the Deformation Characteristics of a Dual Phase Twinning-Induced Plasticity Steel in Warm Working Temperature Regime, Mater. Des., 2012, 40, p 556–561CrossRef
8.
O. Sabokpa, A. Zarei-Hanzaki, H.R. Abedi, and N. Haghdadi, Artificial Neural Network Modeling to Predict the High Temperature Flow Behavior of an AZ81 Magnesium Alloy, Mater. Des., 2012, 39, p 390–396CrossRef
9.
S. Mandal, P.V. Sivaprasad, S. Venugopal, K.P.N. Murthy, and B. Raj, Artificial neural Network Modeling of Composition-Process-Property Correlations in Austenitic Stainless Steels, Mater. Sci. Eng. A, 2008, 485, p 571–580CrossRef
10.
A. Mirzaei, A. Zarei-Hanzaki, N. Haghdadi, and A. Marandi, Constitutive Description of hIGh Temperature Flow Behavior of Sanicro-28 Super-Austenitic Stainless Steel, Mater. Sci. Eng. A, 2014, 589, p 76–82CrossRef
11.
G. Jia, F. Li, Q. Li, H. Li, and Z. Li, A Comparative Study on Arrhenius-Type Constitutive Model and Artificial Neural Network Model to Predict High-Temperature Deformation Behaviour in Aermet100 Steel, Mater. Sci. Eng. A, 2011, 528, p 4774–4782CrossRef
12.
R. Liang and A.S. Khan, A Critical Review of Experimental Results and Constitutive Models for BCC and FCC Metals over a Wide Range of Strain Rates and Temperatures, Int. J. Plast, 1999, 15, p 963–980CrossRef
13.
S. Mandal, M. Jayalakshmi, A.K. Bhaduri, and V.S. Sarma, Effect of Strain Rate on the Dynamic Recrystallization Behavior in a Nitrogen-Enhanced 316L (N), Metall. Mater. Trans. A, 2014, 45, p 5645–5656CrossRef
14.
S. Mandal, A.K. Bhaduri, and V.S. Sarma, Role of Twinning on Dynamic Recrystallization and Microstructure During Moderate to High Strain Rate Hot Deformation of a Ti-Modified Austenitic Stainless Steel, Metall. Mater. Trans. A, 2012, 43, p 2056–2068CrossRef
15.
P. Poelt, C. Sommitsch, S. Mitsche, and M. Walter, Dynamic Recrystallization of Ni-Base Alloys—Experimental Results and Comparisons with Simulations, Mater. Sci. Eng. A, 2006, 420, p 306–314CrossRef
16.
Y.C. Lin, J. Zhang, and J. Zhong, Application of Neural Networks to Predict the Elevated Temperature Flow Behavior of a Low Alloy Steel, Comput. Mater. Sci., 2008, 43, p 752–758CrossRef
17.
Y.C. Lin and X.M. Chen, A Critical Review of Experimental Results and Constitutive Descriptions for Metals and Alloys in Hot Working, Mater. Des., 2011, 32, p 1733–1759CrossRef
18.
W.D. Sun, Y.Q. Zeng, Y.L. Zhao, X. Qi, and Y.F. Ma, Han, Development of Constitutive Relationship Model of Ti600 Alloy Using Artificial Neural Network, Comput. Mater. Sci., 2010, 48, p 686–691CrossRef
19.
A. Sarkar and J.K. Chakravartty, Prediction of Flow Stress in Cadmium Using Constitutive Equation and Artificial Neural Network Approach, J. Mater. Eng. Perform., 2013, 22, p 2982–2989CrossRef
20.
Y. Sun, W.D. Zeng, Y.Q. Zhao, X.M. Zhang, Y. Shu, and Y.G. Zhou, Modeling Constitutive Relationship of Ti40 Alloy Using Artificial Neural Network, Mater. Des., 2011, 32, p 1537–1541CrossRef
21.
N.S. Reddy, Y.H. Lee, C.H. Park, and C.S. Lee, Prediction of Flow Stress in Ti-6Al-4 V Alloy with an Equiaxed α+β Microstructure by Artificial Neural Networks, Mater. Sci. Eng. A, 2008, 492, p 276–282CrossRef
22.
H.Y. Li, D.D. Wei, Y.H. Li, and X.F. Wang, Application of Artificial Neural Network and Constitutive Equations to Describe the Hot Compressive Behavior of 28CrMnMoV Steel, Mater. Des., 2012, 35, p 557–562CrossRef
23.
S. Mandal, P.V. Sivaprasad, S. Venugopal, and K.P.N. Murthy, Constitutive Flow Behaviour of Austenitic Stainless Steels Under Hot Deformation: Artificial Neural Network Modelling to Understand, Evaluate and Predict, Model. Simul. Mater. Sci., 2006, 14, p 1053–1070CrossRef
24.
S. Mandal, P.V. Sivaprasad, and S. Venugopal, Capability of a Feed-Forward Artificial Neural Network to Predict the Constitutive Flow Behavior of As Cast 304 Stainless Steel Under Hot Deformation, J. Eng. Mater. Technol., 2006, 129, p 242–247CrossRef
25.
I.S. Jalham, Modeling Capability of the Artificial Neural Network (ANN) to Predict the Effect of the Hot Deformation Parameters on the Strength of Al-Base Metal Matrix Composites, Compos. Sci. Technol., 2003, 63, p 63–67CrossRef
26.
ASTM E209, Standard Practice for Compression Tests of Metallic Materials at Elevated Temperatures With Conventional or Rapid Heating Rates and Strain Rates, Annual Book of ASTM Standards, ASTM International, PA, 2010.
27.
M.E. Wahabia, L. Gavard, J.M. Cabrera, J.M. Prado, and F. Montheillet, EBSD Study of Purity Effects During Hot Working in Austenitic Stainless Steels, Mater. Sci. Eng. A, 2005, 393, p 83–90CrossRef
28.
F.J. Humphreys and M. Hatherly, Recrystallization and Related Annealing Phenomena, 2nd ed., Pergamon, Oxford, 2004
29.
S. Mandal, S.K. Mishra, A. Kumar, I. Samajdar, P.V. Sivaprasad, T. Jayakumar, and B. Raj, Evolution and Characterization of Dynamically Recrystallized Microstructure in a Titanium-Modified Austenitic Stainless Steel Using Ultrasonic and EBSD Techniques, Philos. Mag., 2008, 88, p 883–897CrossRef
30.
A. Dehghan-Manshadi, M.R. Barnett, and P.D. Hodgson, Recrystallization in AISI, 304 Austenitic Stainless Steel During and After Hot Deformation, Mater. Sci. Eng. A, 2008, 485, p 664–672CrossRef
31.
C. Zener and H. Hollomon, Effect of Strain Rate Upon Plastic Flow of Steel, J. Appl. Phys., 1944, 15, p 22–32CrossRef
32.
P. Changizian, A. Zarei-Hanzaki, and H.R. Abedi, On the Recrystallization Behavior of Homogenized AZ81 Magnesium Alloy: The Effect of Mechanical Twins and γ Precipitates, Mater. Sci. Eng. A, 2012, 558, p 44–51CrossRef
33.
G.Z. Quan, W.Q. Lv, Y.P. Mao, Y.W. Zhang, and J. Zhou, Prediction of Flow Stress in a Wide Temperature Range Involving Phase Transformation for As-Cast Ti-6Al-2Zr-1Mo-1 V Alloy by Artificial Neural Network, Mater. Des., 2013, 50, p 51–61CrossRef
34.
N. Haghdadi, A. Zarei-Hanzaki, A.R. Khalesian, and H.R. Abedi, Artificial Neural Network Modeling to Predict the Hot Deformation Behavior of an A356 Aluminum Alloy, Mater. Des., 2013, 49, p 386–391CrossRef
35.
A. Jenab, A.K. Taheri, and K. Jenab, The Use of ANN to Predict the Hot Deformation Behavior of AA7075 at Low Strain Rates, J. Mater. Eng. Perform., 2013, 22, p 903–910CrossRef
36.
S. Mandal, P.V. Sivaprasad, S. Venugopal, and K.P.N. Murthy, Artificial Neural Network Modeling to Evaluate and Predict the Deformation Behavior of Stainless Steel Type AISI, 304L During Hot Torsion, Appl. Soft Comput., 2009, 9, p 237–244CrossRef
37.
J. Liu, H. Chang, T.Y. Hsu, and X. Ruan, Prediction of the Flow Stress of High-Speed Steel During Hot Deformation Using a BP Artificial Neural Network, J. Mater. Process. Technol., 2000, 103, p 200–205CrossRef
38.
X. Ma, W. Zeng, F. Tian, Y. Sun, and Y. Zhou, Modeling Constitutive Relationship of BT25 Titanium Alloy During Hot Deformation by Artificial Neural Network, J. Mater. Eng. Perform., 2012, 21, p 1591–1597CrossRef
39.
M.P. Phaniraj and A.K. Lahiri, The Applicability of Neural Network Model to Predict Flow Stress for Carbon Steel, J. Mater. Process. Technol., 2003, 141, p 219–227CrossRef
40.
S. Srinivasulu and A. Jain, A Comparative Analysis of Training Methods for Artificial Neural Network Rainfall-Runoff Models, Appl. Soft Comput., 2006, 6, p 295–306CrossRef
Metadaten
Titel
Evaluating the Hot Deformation Behavior of a Super-Austenitic Steel Through Microstructural and Neural Network Analysis
verfasst von
A. Mirzaei
A. Zarei-Hanzaki
M. H. Pishbin
A. Imandoust
Sh. Khoddam
Publikationsdatum
01.06.2015
Verlag
Springer US
Erschienen in
Journal of Materials Engineering and Performance / Ausgabe 6/2015
Print ISSN: 1059-9495
Elektronische ISSN: 1544-1024
DOI
https://doi.org/10.1007/s11665-015-1518-x

Weitere Artikel der Ausgabe 6/2015

Journal of Materials Engineering and Performance 6/2015 Zur Ausgabe

OriginalPaper

Voltammetric Analysis on the Formation of Fe(OH)2 and FeCO3, and on the Reactivity of Passivation of Steel in Carbonate Solutions

EditorialNotes

Editorial

OriginalPaper

Novel Investigation on Nanostructured Multilayer and Functionally Graded Ni-P Electroless Coatings on Stainless Steel

OriginalPaper

Non-destructive Magnetic Evaluation of Laser Weld Quality in Hot Rolled Coils

OriginalPaper

A Hybrid Laser Surface Treatment for Refurbishment of Stress Corrosion Cracking Damaged 304L Stainless Steel

OriginalPaper

Influences of Processing Time and Discharge Current Density During Pulsed Plasma-Oxidizing process of AISI 316L

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise