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Design strategy for optimal iterative learning control applied on a deep drawing process

International journal of advanced manufacturing technology, 2017-01, Vol.88 (1), p.3-18

Endelt, B

2017

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Proposing a new iterative learning control algorithm based on a non-linear least square formulation - Minimising draw-in errors

Journal of physics. Conference series, 2017-09, Vol.896 (1), p.12036

Endelt, B.

2017

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Part-to-Part model predictive control - using a modified Gauss-Newton scheme

IOP conference series. Materials Science and Engineering, 2019-11, Vol.651 (1), p.12077

Endelt, B.

2019

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Online measurement of the surface during laser forming

International journal of advanced manufacturing technology, 2020-03, Vol.107 (3-4), p.1569-1579

2020

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Design strategy for optimal iterative learning control applied on a deep drawing process: Recognising that stamping and deep-drawing operations are repetitive processes—which can learn and improve based on process history

International journal of advanced manufacturing technology, 2017, Vol.88 (1-4), p.3-18

Endelt, B.

2017

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Iterative Learning and Feedback Control Applied on a Deep Drawing Process

International journal of material forming, 2010-04, Vol.3 (Suppl 1), p.25-28

2010

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Multi Stage Strategies for Single Point Incremental Forming of a Cup

International journal of material forming, 2008-04, Vol.1 (Suppl 1), p.1199-1202

2008

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Improving the quality of deep drawn parts using variable blank holder force

International journal of material forming, 2009-08, Vol.2 (Suppl 1), p.809-812

2009

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Feedback control of a forming process and the impact of normal distributed sampling noise

International journal of material forming, 2009-08, Vol.2 (Suppl 1), p.339-342

2009

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Analytic Differentiation of Barlat's 2D Criteria for Inverse Modeling

AIP conference proceedings, 2005, Vol.778 (1)

2005

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Advanced Gradient Based Optimization Techniques Applied on Sheet Metal Forming

AIP conference proceedings, 2005, Vol.778 (1)

2005

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Statistical investigation of a blank holder force distribution system for a multi-step deep drawing process

AIP Conference Proceedings, 2013, Vol.1567 (1), p.1035

2013

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Design strategy for optimal iterative learning control applied on a deep drawing process

Proposing a new iterative learning control algorithm based on a non-linear least square formulation - Minimising draw-in errors

Part-to-Part model predictive control - using a modified Gauss-Newton scheme

Online measurement of the surface during laser forming

Design strategy for optimal iterative learning control applied on a deep drawing process: Recognising that stamping and deep-drawing operations are repetitive processes—which can learn and improve based on process history

Iterative Learning and Feedback Control Applied on a Deep Drawing Process

Multi Stage Strategies for Single Point Incremental Forming of a Cup

Improving the quality of deep drawn parts using variable blank holder force

Feedback control of a forming process and the impact of normal distributed sampling noise

Analytic Differentiation of Barlat's 2D Criteria for Inverse Modeling

Advanced Gradient Based Optimization Techniques Applied on Sheet Metal Forming

Statistical investigation of a blank holder force distribution system for a multi-step deep drawing process

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