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Prediction of Residual Stresses in Heat-Treated Gears Using Finite Element-Based Artificial Neural Networks

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Beschreibung

Determination of the residual stresses resulting from heat treatment processes, such as case hardening, is crucial for the structural integrity assessment of gear units. Experimental methods like deep hole drilling (DHD) are costly, time-consuming, and destructive, while non-destructive methods such as ultrasonic testing offer limited depth resolution. Advances in finite element methods (FEM) provide a promising alternative that does not damage the component. However, each FE computation can take over ten hours, depending on the complexity of the heat treatment and component geometry. By applying a data-driven machine learning approach, one can reduce the computation time from hours to a few seconds.

In this work, heat treatment for various double helical gears is designed and simulated using a commercial FE program. These results are then validated against available data from deep-hole drilling (DHD) experiments. The heat treatment design involves carburizing, hardening, and two tempering cycles, with carburizing and tempering times adjusted based on gear dimensions and case hardening depth (CHD). The FE-based residual stresses are used to train and validate an artificial neural network (ANN) regression model to predict stresses at arbitrary locations for different gear dimensions with their respective heat treatments. Applying the ANN model in fracture assessment significantly decreases evaluation time while maintaining accuracy comparable to FE simulations as well as ensuring safe and reliable operations even at higher pitch line velocities.

Artikelnummer: BR-2025-486

Titel: Prediction of Residual Stresses in Heat-Treated Gears Using Finite Element-Based Artificial Neural Networks
Autor(en): B. Mushtaq, H.R. Bayat, A. Vötterl
DOI: 10.48447/BR-2025-486
Veranstaltung: 57. Tagung des DVM-Arbeitskreises Bruchmechanik und Bauteilsicherheit – Tagung 2025
Jahr der Veröffentlichung: 2025
Publikationsart: Tagungsmanuskript (PDF)
Sprache: Deutsch
Stichwörter: Machine Learning,Gear,Heat Treatment,Residual Stress,Finite Element Analysis

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Deutsch

25,00 €

Nur online erhältlich Neu

Autor(en): S. Gerke, M. Brünig

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Deutsch

25,00 €

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Autor(en): F. Dömling, F. Paysan, V. Schöne, E. Breitbarth

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Deutsch

25,00 €

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Autor(en): V. Schöne, T. Strohmann, F. Paysan, E. Breitbarth

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Deutsch

25,00 €

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Autor(en): M. Hofmann, R. Anderssohn, N. Jesch-Weigel, R. Zielke, T. Wallmersperger

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Deutsch

25,00 €

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Autor(en): F. Paysan, E. Breitbarth

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Deutsch

25,00 €

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Autor(en): S. Münstermann, N. C. Fehlemann

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Berücksichtigung umforminduzierter Schädigung in der Bauteilauslegung

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Deutsch

25,00 €

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Autor(en): R. Boukellif, S. Hilleke, J. Litzba, W. Riesen

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Crack path prediction on integrated raceways under RCF

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Deutsch

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Autor(en): M. Holzwarth, W. Baer, U. Mayer, S. Weihe

Master Curve-Auswertungen und Untersuchung des Größeneffekts für C(T)-Proben - aktuelle Erkenntnisse zur Untersuchung des Master Curve Konzepts für ferritisches Gusseisen mit Kugelgraphit bei dynamischer Beanspruchung (Projekt MCGUSS)

Publikationsart: Tagungsmanuskript (PDF)

Deutsch

25,00 €

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Autor(en): A. Zaiat, C. Kontermann, A. Blug, M. Oechsner, A. Bertz, G. Laskin, F. Conrad, D. Carl

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Deutsch

25,00 €

Nur online erhältlich Neu