An intelligent approach to monitor and control the blanking process
Sensory fusion can be used to infer and analyse complex phenomena and detect changes in a process from a set of measurements. This paper proposes to use Neural Network modelling to monitor product quality of blanking by assessing changes in tool geometry, material quality and tool configuration. The approach may also be used to detect internal and external fractures in the products of blanking. The neural network model is fed with representative parameters, extracted from acoustic signals emitted during fracture, the corresponding waveform and force/displacement data. The neural network model is used, after training to correlate the extracted features of various signals to changes in blanking process parameters such as tool geometry, material hardness and tools clearance. A computerised data-acquisition system, using specialised software and hardware is used to draw from several transducers to record data of the experiments. A total of 192 experiments were performed, using different blanking configurations and materials. This data is used to train the neural network model, which may be integrated with other elements of the control system, to provide a fully automated, real-time supervisory system for blanking. The system could be used to sort components, shutdown the press or alert operators in the event of manufacturing-related defects in the operating cycle.
blanking sensory fusion neural networks force/displacement acoustic emission data acquisition feature extraction