This article examines how force sensors must be positioned to detect incorrect positioning of forging blanks. For this purpose, simulative investigations are carried out on a die. Positions for possible sensor placement are applied in a grid pattern. The recorded force values of the respective sensors are analyzed to identify those sensors that are particularly suitable for reliably detecting incorrect positioning.
industry 4.0, digitalisation, process monitoring
In order to automate the order control of tooth replacement products, an AI model was developed that enables classification into different product classes. The individual tooth replacement products are available in STL files. A mixed-data approach was used for the AI model. The STL file is converted to an image file and passed to a CNN and in parallel, information such as volume and surface dimensions were extracted from the STL file and passed to a ANN. The output from the ANN and the CNN is then combined to produce the final classification of the tooth replacement product.
Automated order control, AI, ANN, image processing, CAD
