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A platform for research: civil engineering, architecture and urbanism
Tools are the key parts in the process of NC milling machine. They are in high-speed processing for a long time and are prone to failure. Aiming at the problems of less tool wear state data,low diagnostic efficiency,high maintenance cost and lack of effective diagnostic methods during CNC machine tool processing,A method of extracting features by wavelet packet analysis and kernel principal component analysis,and using BP Ada Boost algorithm to diagnose tool wear state is proposed.The tool vibration signal and the cutting force signal are collected by installing an acceleration sensor on the machined workpiece of the numerical control machine tool and a force gauge on the workbench; Then the wavelet packet decomposition is performed on the signal to pass the signal through the low-pass filter and the high-pass filter of different dimensions,so that the conditional selection can be performed to form the energy value corresponding to the different frequency bands. The data after the dimension reduction of the kernel principal component analysis is taken as the characteristic parameter of the tool wear state; Finally,the eigenvectors are used to train and validate the BP AdaBoost classification model. The experimental result shows that the BP Ada Boost algorithm can effectively diagnose the wear state of the tool in CNC machine tools compared with the SVM algorithm.
Tools are the key parts in the process of NC milling machine. They are in high-speed processing for a long time and are prone to failure. Aiming at the problems of less tool wear state data,low diagnostic efficiency,high maintenance cost and lack of effective diagnostic methods during CNC machine tool processing,A method of extracting features by wavelet packet analysis and kernel principal component analysis,and using BP Ada Boost algorithm to diagnose tool wear state is proposed.The tool vibration signal and the cutting force signal are collected by installing an acceleration sensor on the machined workpiece of the numerical control machine tool and a force gauge on the workbench; Then the wavelet packet decomposition is performed on the signal to pass the signal through the low-pass filter and the high-pass filter of different dimensions,so that the conditional selection can be performed to form the energy value corresponding to the different frequency bands. The data after the dimension reduction of the kernel principal component analysis is taken as the characteristic parameter of the tool wear state; Finally,the eigenvectors are used to train and validate the BP AdaBoost classification model. The experimental result shows that the BP Ada Boost algorithm can effectively diagnose the wear state of the tool in CNC machine tools compared with the SVM algorithm.
TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP＿ADABOOST ALGORITHM
2019
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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