Experimental studies on drilling of aluminium (AA1050) under dry, minimum quantity of lubricant, and flood-lubricated conditions

Abstract

When considering the environmental issues in machining, the fundamental concern is the use of cutting fluids. Industry and research institutions are looking for ways to reduce the use of cutting fluids for ecological and economical reasons. The disposal of used cutting fluid poses problems to the environment. This has forced engineers to come up with modern ways of cooling technologies during machining.This paper reports on the experimental investigations carried out under dry, minimum quantity of lubricant (MQL), and flood(fully)-lubricated conditions during drilling of aluminium (AA1050). The experiments were planned based on orthogonal arrays, made with prefixed cutting parameters (f and V c ) and different lubricated conditions. An analysis of variance (ANOVA) was carried out to check the validity of the proposed parameters and also their percentage contributions. The results of the tests show that with a proper selection of the range of cutting parameters, it is possible to obtain performances similar to floodlubricated conditions by using MQL.

Experimental and Mathematical Modeling for Prediction of Tool Wear on the Machining of Aluminium 6061 Alloy by High Speed Steel Tools

“…They showed that MQL can reduce the surface roughness by 20% depending upon the level of process parameters and the work piece material. Many researchers have recommended the MQL technique in machining process; [14][15][16] Davim et al and dhar et al, used this technique in a turning process and found that MQL is better than flood cooling.…”

A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling

“…Research on MQL deep drilling is still mainly focused on aluminium, as dry deep drilling is not possible on this material, because of its high adhesion to the drill flutes (Braga et al 2002;Davim et al 2006). On steel, research has mainly focused on experimental tests to define tool wear (Heinemann et al 2007), tool life (Heinemann et al 2006;Filipovic and Stephenson 2006) and surface quality (Davim et al 2006;Zhang and Chen 2009) in relation with MQL and cutting conditions.…”

“…On steel, research has mainly focused on experimental tests to define tool wear (Heinemann et al 2007), tool life (Heinemann et al 2006;Filipovic and Stephenson 2006) and surface quality (Davim et al 2006;Zhang and Chen 2009) in relation with MQL and cutting conditions. Certain studies have attempted to predict chatter phenomena in deep drilling using analytic solutions (Mehrabadi et al 2009).…”

“…Fuzzy logic has been used to predict forces and surface quality on MQL deep drilling of aluminium (Nandi and Davim 2009), drill life (Biglari and Fang 1995;Jantunen and Vaajoensuu 2010) and better cutting conditions (Hashmi et al 2000) in deep drilling of steel with conventional flood cooling. Artificial neural networks (ANN) modelling approaches have been used for predicting burr size (Davim et al 2006) and drill wear (Sanjay et al 2005) in deep drilling of steel with conventional flood cooling. Finally, genetic algorithms have been applied to maximise metal removal rate in a standard drilling process (Zang et al 2006).…”

The influence of cooling method on cutting power during turning of 316L austenitic steel

“…W obu metodach ilość CCS w postaci aerozolu nie przekracza na ogół 50 ml/h [3,5] i metody te znalazły już zastosowanie w procesach: toczenia [3,5,6], frezowania [4], wiercenia [7] i szlifowania [1]. W metodzie MQL medium czynnym jest przede wszystkim olej [1, 3÷5, 7], stąd jej głównym zadaniem jest smarowanie.…”