Experimental verification of a deep drawing tool system for adaptive blank holder pressure distribution

Tommerup, Søren; Endelt, Benny Ørtoft

doi:10.1016/j.jmatprotec.2012.06.015

Cited by 42 publications

(19 citation statements)

References 14 publications

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“…• The control system was implemented using a National Instruments CompactRIO real time platform type: NI cRIO-9074. • Four pressure supply units were designed and each is able to deliver a pressure of 50 MPa (Tommerup and Endelt, 2012). The reference draw-in trajectories were produced using the blank-holder force and cavity pressures listed in Table 5.…”

Section: Preliminary Experimental Resultsmentioning

confidence: 99%

“…The cavities in the shimming plate thus serve as actuators (see Figs. 2 and 4) enabling adaptive control of the blank-holder pressure distribution during the punch stroke (Tommerup and Endelt, 2012;Nielsen, 2002). A picture of the physical setup can be seen in Fig.…”

Section: Tool Design and Input/output Variablesmentioning

confidence: 99%

“…3. Picture of the shimming plate, note the cavities which can be pressurized enabling distributed blank-holder force (Tommerup and Endelt, 2012). …”

Section: Finite Element Model Of the Systemmentioning

confidence: 99%

See 2 more Smart Citations

A novel feedback control system – Controlling the material flow in deep drawing using distributed blank-holder force

Endelt

Tommerup

Danckert

2013

Journal of Materials Processing Technology

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Section: Preliminary Experimental Resultsmentioning

confidence: 99%

Section: Tool Design and Input/output Variablesmentioning

confidence: 99%

See 1 more Smart Citation

A novel feedback control system – Controlling the material flow in deep drawing using distributed blank-holder force

Endelt

Tommerup

Danckert

2013

Journal of Materials Processing Technology

Self Cite

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“…The influence of space and time variation schemes of blank holder forces on the process results is examined e.g. in [30], [26] and [33]. The goal in the example application is to find optimal blank holder forces regarding the process result and depending on the current, partially observable, process condition.…”

Section: Introductionmentioning

confidence: 99%

Model-free Adaptive Optimal Control of Episodic Fixed-horizon Manufacturing Processes Using Reinforcement Learning

Dornheim

Link

Gumbsch

2019

Int. J. Control Autom. Syst.

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A self-learning optimal control algorithm for episodic fixed-horizon manufacturing processes with time-discrete control actions is proposed and evaluated on a simulated deep drawing process. The control model is built during consecutive process executions under optimal control via reinforcement learning, using the measured product quality as reward after each process execution. Prior model formulation, which is required by state-of-the-art algorithms from model predictive control and approximate dynamic programming, is therefore obsolete. This avoids several difficulties namely in system identification, accurate modelling, and runtime complexity, that arise when dealing with processes subject to nonlinear dynamics and stochastic influences. Instead of using pre-created process and observation models, value function-based reinforcement learning algorithms build functions of expected future reward, which are used to derive optimal process control decisions. The expectation functions are learned online, by interacting with the process. The proposed algorithm takes stochastic variations of the process conditions into account and is able to cope with partial observability. A Q-learning-based method for adaptive optimal control of partially observable episodic fixed-horizon manufac-turing processes is developed and studied. The resulting algorithm is instantiated and evaluated by applying it to a simulated stochastic optimal control problem in metal sheet deep drawing.

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“…Furthermore, Rode Ibrahim Demirci, Cemal Esner, Mustafa Yasar [5][6], investigated cracking criteria in deep drawing. According to their research informed, the cracking would appear, when the deep drawing force did not exceed the maximum strength of material or no higher than tensile strength of material [5][6][8] [9][10] [11], and the radial material flow was not very obstructed.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Semi-Active the Blank Holder Force Control to Prevent Wrinkling and Cracking in Deep Drawing Process

Candra

Batan

Berata

et al. 2014

AMM

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This paper presents simulation of drawing force and thickness deformation in deep drawing which employs semi-active blank holder force system, to solve the problem of cracking and wrinkling. The method of slab with feed back control failure criteria, was employed to make the modeling system and the semi-active blank holder to prevent wrinkling and cracking in forming low carbon steel sheet, without lubrication (μ=0.4). In this study, the mechanical properties of the material were chosen since that they equivalent to those of low carbon steel with its thickness of 0.2 mm, k = 572 N/mm2, UTS = 391 N/mm2, yield stress = 309 N/mm2 and n = 0.2. The diameter and the depth of the cylindrical cup-shaped product were 40 mm and 10 mm, respectively. Results from simulation have shown that the semi-active blank holder system can control very responsive against changing of deformation condition. The optimum of initial blank holder force is approximately 3000 N up to 4000 N. In the early stages (initial stroke), blank holder force system could be responsive to prevent cracking, and at the end of the punch stroke, it is very effective to prevent wrinkling. Simulation of semi-active blank holder force control system is excellent in model formation to prevent cracking and wrinkling.

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Experimental verification of a deep drawing tool system for adaptive blank holder pressure distribution

Cited by 42 publications

References 14 publications

A novel feedback control system – Controlling the material flow in deep drawing using distributed blank-holder force

A novel feedback control system – Controlling the material flow in deep drawing using distributed blank-holder force

Model-free Adaptive Optimal Control of Episodic Fixed-horizon Manufacturing Processes Using Reinforcement Learning

Simulation of Semi-Active the Blank Holder Force Control to Prevent Wrinkling and Cracking in Deep Drawing Process

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