Abstract
This work aims at determining the right moment to stop single-point dressing the grinding wheel in order to optimize the grinding process as a whole. Acoustic emission signals and signal processing tools are used as primary approach. An acoustic emission (AE) sensor was connected to a signal processing module. The AE sensor was attached to the dresser holder, which was specifically built to perform dressing tests. In this work there were three types of test where the edit parameters of each dressing test are: the passes number, the dressing speed, the width of action of the dresser, the dressing time and the sharpness. Artificial Neural Networks (ANNs) technique is employed to classify and predict the best moment for stopping the dressing operation. During the ANNs use, the results from Supervised Neural Networks and Unsupervised Neural Networks are compared.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
