Publication date: Jan 31, 2022
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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File name | Size | Description |
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220122_SIMKI_Training_Datasets.zip
MD5md5:a5233cc5e8b5d5fb201611175f5e4b61
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17.0 MiB | This archive contains the training and validation datasets, including the trained neural network of the SIMKI-Project of Hochschule-Aalen. |
2022.16 (version v1) [This version] | Jan 31, 2022 | DOI10.24435/materialscloud:74-x1 |