| Issue |
MATEC Web Conf.
Volume 175, 2018
2018 International Forum on Construction, Aviation and Environmental Engineering-Internet of Things (IFCAE-IOT 2018)
|
|
|---|---|---|
| Article Number | 02002 | |
| Number of page(s) | 5 | |
| Section | Building Equipment Automation | |
| DOI | https://doi.org/10.1051/matecconf/201817502002 | |
| Published online | 02 July 2018 | |
Identification of process damping for chatter prediction in milling
1
Quanzhou Institute of Technology, Department of Mechanical Manufacturing and Automation, 362268 Jinjiang, Fujian, China
2,a
Fujian Agriculture and Forestry University, College of Transportation and Civil Engineering, 350108 Fuzhou, Fujian, China
*
Corresponding author : aLcf66131@fafu.edu.cn
Traditionally, forecasting stability lobe diagram in milling is limited by complex damping identification procedures, so only structural damping from the impact experiment is used for predicting stability lobe diagram in most milling cases. In this study, by using the mathematical expressions among damping ratio, “critical limiting depth of cut” and “worst spindle speed”, it is shown that the predicted “critical limiting depth of cut” based on the structural damping divided by the measured “critical limiting depth of cut” can be approximately equal to the structural damping divided by the total damping. Based on this relationship, it is easy to estimate the total damping or process damping from the experiment within the selected spindle speeds. In practice, this paper presents an easy method for predicting stability lobe diagram using the total damping. At the same time, experiments have confirmed that using the prediction model of total damping can more accurately predict the stability lobe diagram.
© The Authors, published by EDP Sciences 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.