Self-Learning Sheet Metal Forming System (LearnForm)
Self-Learning Sheet Metal Forming System
Start date: 01 Apr 2009,
End date: 31 Mar 2012
The automotive and white goods market tends towards individual customer solutions with product variants in small batch production, for which the forming system needs special setups, parameter change in self-learning control systems and high skilled operators. The self-learning sheet metal forming system LearnForm aims at an innovative knowledge-based drawing process with integrated multi sensors and actuators for adapting the control system strategy to changed material properties and product variants. The concept of the project LearnForm bases on the following main ideas: 1) a self-learning sheet metal forming system based on work piece energy and thermal quality control, 2) intelligent drawing dies including multi-sensors, 3) multi die cushion axes with adaptronic force oscillation actuators and 4) an open architecture motion control system extended by self-learning control strategies. Three tasks of self-learning control are the sliding friction, forming and clamping tasks supervised by the energy level with thermo camera quality check. Demonstrators are intelligent drawing dies for automotive and white goods and self-learning process control software with multi criteria optimizing strategies. The industrial leadership is performed by 5 companies (3OEM, 2SME) with their market leader knowledge and product programme. RTD partners co-operate with outstanding applied and basic interdisciplinary research experience. The work packages include simulation, measuring, programming, training and testing tasks. The project consortium has estimated the high RTD risk and total project costs to 4.77M€. The total project exploitation from 8 partners is estimated to 25M€ in the third year. The impact from the LearnForm results in the European market of knowledge-based sheet forming presses and dies, industrial process control is characterized by an annual increased turnover of 57.4M€ and by annual reduced manufacturing costs in automotive and white goods of 49.9M€.
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