Fast models for online-optimization during open die forging

Recker, Dominik; Franzke, Martin

doi:10.1016/j.cirp.2011.03.142

Cited by 20 publications

(15 citation statements)

References 3 publications

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“…Therefore, fast models to calculate the equivalent strain, temperature and the microstructure along the core fiber of the workpiece were transferred into the software "FAST" (Forging AnalysiS Tool). [3] The description of the equivalent strain along the core by FAST is based on the assumption that the distribution of the equivalent strain in the forming zone of one stroke can be described by a sine 2 -distribution, [3] see Figure 1. The empiric parameters which are needed for the model can be determined by FEA sensitivity analyses.…”

Section: Fast Models For Open Die Forgingmentioning

confidence: 99%

“…To be able to receive results of the equivalent strain as well, more sophisticated models are required. Therefore, fast models to calculate the equivalent strain, temperature and the microstructure along the core fiber of the workpiece were transferred into the software “FAST” (Forging AnalysiS Tool) …”

Section: Introductionmentioning

confidence: 99%

“…The description of the equivalent strain along the core by FAST is based on the assumption that the distribution of the equivalent strain in the forming zone of one stroke can be described by a sine 2 ‐distribution, see Figure . The empiric parameters which are needed for the model can be determined by FEA sensitivity analyses.…”

Section: Introductionmentioning

confidence: 99%

“…In the presented work a combined 1D/2D temperature model was realized which is based on the works of Recker . The longitudinal cross‐section of the workpiece was spatially discretized.…”

Section: Introductionmentioning

confidence: 99%

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Online Visualization during Open Die Forging and Optimization of Pass Schedules**

Rosenstock

Recker

Franzke

et al. 2014

steel research int.

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By using fast calculation models, the equivalent strain, temperature, and average austenitic grain size in the center of an ingot can be determined. When used in an online process monitor by coupling with a measuring system, these models are fast enough to visualize the current state of an ingot during forging. Based on a model forging process, the use of the program is demonstrated. Comparing results from metallography of the forged billet with the calculated grain size, a good agreement could be achieved (deviation of 6–24%). Even before the forging process, the presented automatic pass schedule optimization can be applied. Through this, a process could be optimized concerning a high homogeneous equivalent strain and a low number of strokes. As result of the optimization, an increasing grain size and temperature could be observed. Thus it could be useful to change other process parameters, like lowering the furnace temperature.

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Section: Fast Models For Open Die Forgingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In the presented work a combined 1D/2D temperature model was realized which is based on the works of Recker . The longitudinal cross‐section of the workpiece was spatially discretized.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online Visualization during Open Die Forging and Optimization of Pass Schedules**

Rosenstock

Recker

Franzke

et al. 2014

steel research int.

View full text Add to dashboard Cite

show abstract

“…To do so, high-fidelity process models can be used to predict the final state of the process based on the intermediate measurements. Both theoretical models such as a homogenized energy model for shape memory alloys [2], a finite element model for bending [3] and an analytical strain model for open die forging [4] as well as empirical models such as a linearized model of tool path influence in flexible metal forming [5] and a neural network [6] or a regression model [7] for bending have been used for this purpose. The development of these models is a key factor for successful increase of production accuracy through closed-loop control.…”

Section: Introductionmentioning

confidence: 99%