Condition assessment for automatic tool changer based on sparsity-enabled signal decomposition method

Automatic tool changer (ATC) is one of the key basic parts in CNC machining centers, and the globoidal indexing cam and the groove cam are the functional units for tool changing. Thus the condition monitoring is important for highly efficient and precision machining. In this paper, a condition monitoring system is constructed for the ATC, especially for the globoidal indexing cam, including vibration signal acquisition, fault feature extraction and localization, and condition assessment. In the constructed system, sparsity-enabled signal decomposition method is introduced to extract transient component and reduce noises in the complex vibration signals, and the transient component is always a key feature for fault localization. Simulation study shows that the sparsity-enabled signal decomposition method is effective in transient feature extraction. The experimental application in condition assessment for the ATC demonstrates that the constructed condition monitoring system has the potential to assess the working condition of the ATC in practical application.