2026-04-29T22:50:11Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:1202 2022-01-31T15:56:37Z openaire_data community-mcarchive

Feldmann, Sebastian Schmiedt, Michael Schlosser, Julian Rimkus, Wolfgang Stempfle, Tobias Feldmann, Sebastian Schmiedt, Michael Schlosser, Julian Rimkus, Wolfgang Stempfle, Tobias 2022-01-31 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. text/markdown application/zip https://doi.org/10.24435/materialscloud:74-x1 oai:materialscloud.org:1202 mcid:2022.16 eng Materials Cloud https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:yh-9g info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Artificial Intelligence Gocator Defect Detection Recursive quality optimization of a smart forming tool under use of perception based hybrid datasets for training of a deep neural network info:eu-repo/semantics/other