2022
DOI: 10.1016/j.procir.2022.04.079
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Numerical investigation and modeling of residual stress field variability impacting the machining deformations of forged part

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Cited by 5 publications
(2 citation statements)
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“…Their 2D FEM thermomechanical simulations were then computed, in Abaqus software, for seven potential defects or deviations of key process parameters of the quenching and cold compression of the cruciform (such as quenching temperature or die positioning errors), cf. [13]. Concerning the predictions of part deformation, as explained in 3.2, machine learning from the previous parts deformations is suitable.…”
Section: Digital Twin Of Cruciform Forged Partmentioning
confidence: 99%
See 1 more Smart Citation
“…Their 2D FEM thermomechanical simulations were then computed, in Abaqus software, for seven potential defects or deviations of key process parameters of the quenching and cold compression of the cruciform (such as quenching temperature or die positioning errors), cf. [13]. Concerning the predictions of part deformation, as explained in 3.2, machine learning from the previous parts deformations is suitable.…”
Section: Digital Twin Of Cruciform Forged Partmentioning
confidence: 99%
“…The approaches rely on the hypothesis that the shape of the bulk RS field is identical between parts and that only the magnitude varies. It seems true for rolled plates, but not for forged parts where the RS field shape can vary [13]. Therefore, a hybrid DT approach, which takes full advantage of data-driven and physic-based approaches, seems necessary to tackle the complex issue of the RS field variability in forged parts.…”
Section: Introductionmentioning
confidence: 99%