Reduction of random seams in hot rolling through FEM based sensitivity analysis

Ji, Meixing; Shivpuri, Rajiv

doi:10.1016/j.msea.2006.03.071

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Deterministic and Probabilistic Predictions1

Classical Models For Process Control Applications1

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Cited by 7 publications

(2 citation statements)

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“…More formal use of the design methodology and artificial intelligence (AI) in the optimal design of a die or a forming process include sensitivity analysis (e.g. in the design of hot rolling passes [102]), genetic algorithmic search (e.g. optimal design of drawing dies [179] and multi-stage processes [180]), knowledge based design, ANN, fuzzy reasoning [200] and reverse engineering.…”

Section: Deterministic and Probabilistic Predictionsmentioning

confidence: 99%

Metal forming beyond shaping: Predicting and setting product properties

et al. 2015

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Section: Deterministic and Probabilistic Predictionsmentioning

confidence: 99%

Metal forming beyond shaping: Predicting and setting product properties

et al. 2015

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“…For multistage deformation the effect of work hardening of the previous deformations and of recrystallization on the dislocation density has to be taken into account. To do so the concept of accumulated strain is used by many authors [15,18,25]. The strain of each pass is accumulated to ε ACC .…”

Section: Classical Models For Process Control Applicationsmentioning

confidence: 99%

A new unified approach for modeling hot rolling of steel Part 1: Comparison of models for recrystallization

Orend,

Hagemann,

Klose

et al. 2014

Preprint

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Models for the microstructure evolution during hot rolling are reviewed. The basic macroscopic phenomena related to recrystallization are summarized. Constitutive models based on semi empirical equations are compared to more sophisticated models based on cellular automata, vertex and Monte-Carlo-Potts methods. The applicability of each kind of model approach for online and offline process control in steel industry is discussed. While constitutive models are still state-of-the-art for online process control, mesoscale models with a spatial representation of the microstructure can provide better predictive capabilities at the cost of long computation times. To fill this gap a new approach based on modeling the interaction of an ensemble of multiple grains is outlined and first simulation results are presented. The proposed approach allows the unified modeling of dynamic, static and metadynamic recrystallization as well as grain growth.

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Knowledge discovery in steel bar rolling mills using scheduling data and automated inspection

Agarwal

Shivpuri

2013

J Intell Manuf

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Reduction of random seams in hot rolling through FEM based sensitivity analysis

Cited by 7 publications

References 9 publications

Metal forming beyond shaping: Predicting and setting product properties

Metal forming beyond shaping: Predicting and setting product properties

A new unified approach for modeling hot rolling of steel Part 1: Comparison of models for recrystallization

Knowledge discovery in steel bar rolling mills using scheduling data and automated inspection

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