Sebastian Feldmann, Michael Schmiedt, Johann Jung, Julian Marc Schlosser, Tobias Stempfle, Christian Rathmann and Wolfgang Rimkus, Aalen University, Germany

This paper concerns unpublished results obtained from the SIMKI (2020) R&D project at the Department of Mechanical Engineering at Aalen University of Applied Science, Germany. The following text generally discusses the development results of the AI-based CNC parameter identification and optimisation tool AICNC. The identification tool supports the AI-based optimisation of milling machine process parameters when using unknown material compositions. The process parameters are determined by a specific test pattern designed to be automatically analysed in real time by a pre-trained perception-based deep learning algorithm. The tool provides the advantage of obtaining real-time quality information due to AI-based quality assessment and the automated identification of material-dependent milling process parameter sets, even for unknown processing material.

Artificial Intelligence, Process Optimisation, CNC-Milling, Parameter Prediction, Image Processing.