Recursive quality optimization of a smart forming tool under use of perception based hybrid datasets for training of a deep neural network
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- 1. University of Applied Science - Aalen, Beethovenstr. 1, 73430 Aalen, Germany
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Description
In industrial metal forming processes, the generation of datasets for inline and optical quality assessment is expensive and time-consuming. Within the research project SimKI, conventional metal forming plants were digitalized under use of perception-based sensors in combination with a completely redesigned forming tool. The integration of optical quality observation methods connected with a retrofitting approach of the press tool provides the opportunity to generate an information-feedback loop that predicts part defects prior to their occurrence. The SimKI-method additionally combines conventional statistical measurement methods with AI-based defect detection algorithms that are trained by a) generic datasets of a finite-element simulation, b) real component images of a 3D imaging device, and c) a combination of both. The generated datasets are used to accelerate the training of a DNN-based algorithm in order to identify the position and deviation from the agreed quality. The high degree of innovation is based on obtaining real-time component quality information under use of AI-based optical quality assessment, which in turn provides information to the control algorithm of the smart forming tool.
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References
Journal reference S. Feldmann, M. Schmiedt, J. M. Schlosser, W. Rimkus, T. Stempfle, Recursive Quality Optimization of a Smart Forming Tool under use of Perception Based Hybrid Datasets for Training of a Deep Neural Network (submitted).