MATEC Web Conf.
Volume 82, 20162016 International Conference on Design, Mechanical and Material Engineering (D2ME 2016)
|Number of page(s)||9|
|Section||Chapter 1: Mechanical Engineering|
|Published online||31 October 2016|
Surface Roughness Prediction in Grinding: a Probabilistic Approach
1 Department of Mechanical Engineering, Centre for Advanced Composite Materials, University of Auckland, Auckland, 1010, New Zealand
2 Department of Mechanical Engineering, B.I.E.T., Jhansi, Uttar Pradesh, India, email@example.com
Surface quality of machined components is one of the most important criteria for the assessment of grinding processes. The importance of surface finish of a product depends upon its functional requirements. Since surface finish is governed by many factors, its experimental determination is laborious and time consuming. So the establishment of a model for the reliable prediction of surface roughness is still a key problem for grinding. In this study, a new analytical surface roughness model is developed on the basis of the stochastic nature of grinding processes. The model is governed mainly by the random geometry and the random distribution of cutting edges on the wheel surface having random grain protrusion heights. A simple relationship between the surface roughness and the chip thickness was obtained, which was validated by the experimental results using AISI 4340 steel in surface grinding.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 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.