Automated driving for individualized sheet metal part production—A neural network approach

• A special driving process for individualized sheet metal part production is investigated.
• We present a novel approach for tool path generation adopting neural networks.
• Training data sets are generated and the neural network architecture is implemented.
• Using network generated tool paths, L-shaped profiles are produced fully automated.
• An enhanced concept for the production of arbitrary sheet metal shapes is designed.

The manufacturing of individualized sheet metal components is one of the most important issues in industrial sheet metal working. Incremental forming methods, in particular driving, offer the opportunity for achieving this objective. However, these manual processes are very difficult to automate, as a result of their complexity and user interactivity. To resolve this problem, a knowledge-based approach is presented, which utilizes a special type of driving process. Initially, a neural network architecture is established which delivers manufacturing strategies allowing part production for simple component shapes. After providing a method for training data generation, training sessions are carried out. Strategies, computed by trained networks, are adopted for processing sheet blanks which are used for evaluating the framework. Finally, the developed procedure is generalized, and a concept is designed which allows a transfer, in order to facilitate the production of arbitrary individualized sheet metal parts.