Original Paper

Computational Mechanics

, Volume 47, Issue 5, pp 573-590

First online:

Forming forces in single point incremental forming: prediction by finite element simulations, validation and sensitivity

  • C. HenrardAffiliated withArGEnCo, Université de LiègeSamtech S.A. Email author 
  • , C. BouffiouxAffiliated withArGEnCo, Université de Liège
  • , P. EyckensAffiliated withMTM, Katholieke Universiteit Leuven
  • , H. SolAffiliated withMEMC, Vrije Universiteit Brussel
  • , J. R. DuflouAffiliated withPMA, Katholieke Universiteit Leuven
  • , P. Van HoutteAffiliated withMTM, Katholieke Universiteit Leuven
  • , A. Van BaelAffiliated withMTM, Katholieke Universiteit LeuvenKHLim (Limburg Catholic University College)
  • , L. DuchêneAffiliated withArGEnCo, Université de Liège
  • , A. M. HabrakenAffiliated withArGEnCo, Université de Liège

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The aim of this article is to study the accuracy of finite element simulations in predicting the tool force occurring during the single point incremental forming (SPIF) process. The forming of two cones in soft aluminum was studied with two finite element (FE) codes and several constitutive laws (an elastic–plastic law coupled with various hardening models). The parameters of these laws were identified using several combinations of a tensile test, shear tests, and an inverse modeling approach taking into account a test similar to the incremental forming process. Comparisons between measured and predicted force values are performed. This article shows that three factors have an influence on force prediction: the type of finite element, the constitutive law and the identification procedure for the material parameters. In addition, it confirms that a detailed description of the behavior occurring across the thickness of the metal sheet is crucial for an accurate force prediction by FE simulations, even though a simple analytical formula could provide an otherwise acceptable answer.


Single point incremental forming Finite element modeling Force prediction Material parameters identification Inverse modeling