2024: Testing of artificial intelligence in the control of a bicycle tire studding machine

The aim of the project is to propose and test a possible solution using artificial intelligence, where stud slots are detected on the tire rolling surface and studs with the correct orientation are installed. After installing the studs, the AI solution also performs a quality control to ensure that all the corresponding slots are filled and the correct orientation of the studs is ensured. It is necessary to validate different possible deep learning models such as convolutional neural network or recurrent neural networks that can be used for image recognition tasks and would be suitable for identifying tire elements. For this purpose, the peculiarities of the bicycle tire studding process and the capabilities of the studding machine must be mapped. Hardware and software solutions must be selected according to the requirements of the production environment. At first, the training of the selected AI solution is carried out in the university laboratory. After laboratory tests the selected AI solution is trained on the production device and a production test is conducted. Finally, the experimental data and carried out developments are summarized and the results are made public. The desired result is a tested possible solution that is capable of performing autonomous quality control and error elimination on a bicycle tire studding machine during the production process on an ongoing basis without human intervention.