Material identification based on machine-learning algorithms for hybrid workpieces during cylindrical operations

New design concepts for high-performance components are part of the current research. Because of various material properties and chemical composition, the cutting characteristics and chip formation mechanisms change during the machining process. Thus, it can be mandatory to identify the material and adapt the process parameters during machining. As a result, the workpiece quality is optimized while increasing the tool life. Therefore, this paper investigates a new approach to determine the machined material in-process by machine-learning. A cylindrical turning process is performed for friction welded EN-AW6082/20MnCr5 and C22/41Cr4 shafts. Acceleration and process force signals as well as control signals are measured and monitoring features are generated. These features are ranked and selected based on the information value by the joint mutual information method. Afterwards, four machine-learning models are trained to identify the machined material based on the signal features. The monitoring quality is evaluated during various cylindrical turning processes and the most appropriate machine-learning algorithm is determined. Thus, a new methodology for in-process material identification in CNC turning machines based on signal analysis and machine-learning algorithm is proposed.