2007
DOI: 10.4028/www.scientific.net/msf.558-559.1201
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Modeling Recrystallization for 3D Multi-Pass Forming Processes

Abstract: The present work concerns the simulation of metallurgical evolutions in 3D multi-pass forming processes. In this context, the analyzed problem is twofold. One point refers to the management of the microstructure evolution during each pass or each inter-pass period and the other point concerns the management of the multi-pass aspects (different grain categories, data structure). In this framework, a model is developed and deals with both aspects. The model considers the microstructure as a composite made of a g… Show more

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Cited by 2 publications
(2 citation statements)
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“…All the models were used "as is" with the default set of parameters for IN718 available in the material libraries of each software. For the detailed description of the models and values of the model parameters please refer to description of the models [9][10][11]. The initial grain size of the material was set to 17µm and temperature limit of 950°C was used for the initiation of microstructure evolution in all the models.…”
Section: Methodsmentioning
confidence: 99%
“…All the models were used "as is" with the default set of parameters for IN718 available in the material libraries of each software. For the detailed description of the models and values of the model parameters please refer to description of the models [9][10][11]. The initial grain size of the material was set to 17µm and temperature limit of 950°C was used for the initiation of microstructure evolution in all the models.…”
Section: Methodsmentioning
confidence: 99%
“…3) Between these two extreme approaches, specific treatments of microstructures obtained after each pass can be undertaken, in order to reduce the number of grain families we have to manage, without coming up to just one family as specified above. An example of this methodology can be illustrated by ANAN and col. approach [6], and developed again by TEODORESCU and col. [7].…”
Section: Strategies Available For Microstructure Prediction In Incremmentioning
confidence: 99%