Experimental Setup for Multi-Sensor Fusion and Data Correlation Analysis During CNC Steel Turning Process

This paper discusses a preliminary setup for an ongoing research project with goals of off-line modeling and optimization for a CNC turning process of AISI 4137 steel alloy; followed by online monitoring, optimization, and control of the machining process. A full factorial Design Of Experiment (DOE) of three machining parameter factors was created in Minitab™ and Analysis of Variance was performed, in order to determine which parameters influenced the machining process the most. Accelerometers, acoustic emission sensors and force sensors have given researchers insights into the relationships between mechanical vibration and tool condition during the turning process. Similarly, correlations have been recognized between electrical power consumption, machining forces, tool temperature, and tool condition. While monitoring the machining process with sophisticated force and acceleration sensors is effective, implementation in a large scale factory environment may not be an economical solution to online monitoring and control. Finding an ideal combination of sensors capable of monitoring significant factors that affect the CNC steel turning process will allow process optimization and reduce the cost of machining.