Recursive quality optimization of a smart forming tool under the use of perception based hybrid datasets for training of a Deep Neural Network

Abstract

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 the use of perception-based 3D-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 before their occurrence. Additionally, the SimKI-method combines conventional statistical measurement methods with AI-based defect detection algorithms that are trained by generic datasets of a finite-element simulation, real component images of a 3D imaging device, and a combination of both. The generated datasets are used to accelerate the training of a DNN-based algorithm 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 the use of AI-based optical quality assessment, which in turn provides information to the control algorithm of the smart forming tool.