Abstract
The amplitude of grinding vibration increases gradually throughout the grinding wheel wear process. In the meantime the predominant vibration frequency shifts in a region close to a natural frequency of the system. The complex time-varying pattern of vibrations makes it a problem to objectively identify when the grinding vibration becomes unacceptable and when the wheel should be redressed. A neural network approach method was proposed in this paper to identify the wheel life. The signal data were pre-treated by eight-band-pass filters, which covered the whole frequency range of the grinding chatter. These pre-treated data were used as the inputs to the neural network. By training the neural network, an objective criterion can be determined for the wheel redress life.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.