ONLINE TOOL WEAR PREDICTION MODELS IN MINIMUM QUANTITY LUBRICATION

Authors

  • S.Narayana Rao

Keywords:

tool wear; MQL; cutting fluids; regression model; artificial neural networks

Abstract

With the problems in usage of cutting fluids, the use of Minimum Quantity Lubrication (MQL) has gained prominence. Though several mathematical models have been postulated in literature on dry cutting, models that deal with cutting fluids are very rare and the models on MQL are seldom found. The present work tries to discuss regression and artificial neural network models postulated on influence of MQL on tool wear, while machining AISI 1040 steel using HSS tool. The proposed models were validated with the experimental results.

How to Cite

S.Narayana Rao. (2011). ONLINE TOOL WEAR PREDICTION MODELS IN MINIMUM QUANTITY LUBRICATION. Global Journals of Research in Engineering, 11(5), 13–18. Retrieved from https://engineeringresearch.org/index.php/GJRE/article/view/221

ONLINE TOOL WEAR PREDICTION MODELS IN  MINIMUM QUANTITY LUBRICATION

Published

2011-10-15