Concept of an automated framework for sheet metal cold forming

This article describes a method to replace the current manual cold forming process of curved panels in ship building by a fully automated process control using a conventional press and crane setup. Basing on a simplified geometrical model and a self-learning artificial neural network the sheet metal forming as well as the derived forming strategy can be predicted. To position the workpiece that is suspended by four trolleys to the service point a flatness-based control algorithm has been developed. The algorithm enables sway-free movements. To monitor the production process successively, two 3D laser scanners are used. Reference marks applied on the workpiece surface are enabling the alignment calculation within the work space of the automated press as well as the plate contour at different bending lines. The successive laser scans recalibrate the neural network used for the local forming prediction and for forming process planning. The higher-level control assures the real-time capability of the control algorithms and the processing of all sensor data. Sensor multiplicity is used to supervise mechanism security and retains the acquired information in an all-embracing database to be considered in planning of future forming processes. The resulting database replaces the many years of experience of the workers.