Multi-sensor data fusion framework for CNC machining monitoring

Reliable machining monitoring systems are essential for lowering production time and manufacturing costs. Existing expensive monitoring systems focus on prevention/detection of tool malfunctions and provide information for process optimisation by force measurement. An alternative and cost-effective approach is monitoring acoustic emissions (AEs) from machining operations by acting as a robust proxy. The limitations of AEs include high sensitivity to sensor position and cutting parameters. In this paper, a novel multi-sensor data fusion framework is proposed to enable identification of the best sensor locations for monitoring cutting operations, identifying sensors that provide the best signal, and derivation of signals with an enhanced periodic component. Our experimental results reveal that by utilising the framework, and using only three sensors, signal interpretation improves substantially and the monitoring system reliability is enhanced for a wide range of machining parameters. The framework provides a route to overcoming the major limitations of AE based monitoring. •AE sensors are highly sensitive to sensor position and cutting parameters.•A multi-sensor data fusion framework for CNC machining monitoring is proposed.•The framework is able to enhance the periodic component and SNR of the signal.•We study the robustness of the framework for a wide range of machining parameters.•With only three sensors it is possible to improve the signal interpretation.