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

• Published in: Journal of physics. Conference series Vol. 896; no. 1; pp. 12036 - 12043
• Main Author: Endelt, B.
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.09.2017
• Subjects: Algorithms; Control algorithms; Control stability; Control theory; Deep drawing; Error reduction; Feedback control; Geometry; Iterative methods; Least squares; Machine learning; Material properties; Metal forming; Metal sheets; Physics; Process parameters
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/896/1/012036

Forming operation are subject to external disturbances and changing operating conditions e.g. new material batch, increasing tool temperature due to plastic work, material properties and lubrication is sensitive to tool temperature. It is generally accepted that forming operations are not stable over time and it is not uncommon to adjust the process parameters during the first half hour production, indicating that process instability is gradually developing over time. Thus, in-process feedback control scheme might not-be necessary to stabilize the process and an alternative approach is to apply an iterative learning algorithm, which can learn from previously produced parts i.e. a self learning system which gradually reduces error based on historical process information. What is proposed in the paper is a simple algorithm which can be applied to a wide range of sheet-metal forming processes. The input to the algorithm is the final flange edge geometry and the basic idea is to reduce the least-square error between the current flange geometry and a reference geometry using a non-linear least square algorithm. The ILC scheme is applied to a square deep-drawing and the Numisheet'08 S-rail benchmark problem, the numerical tests shows that the proposed control scheme is able control and stabilise both processes.