A λ-cut approximate algorithm for goal-based bilevel risk

Abstract

In this study, the effect of MQL application on the tool wear, surface roughness and chip formation in turning Aluminum alloy 6061 is investigated. Experiments were carried out by plain turning of an Aluminum bar with varying depth of cut, cutting speeds (spindle speed) and cutting environments (Dry, Wet and MQL). A newly designed, cost efficient and portable MQL setup was developed for this study. For each experimental trial, five passes were carried out in order to promote the formation of tool wear. After each pass, the tool surface was examined under a microscope and the surface roughness was measured using a stylus based surface tester. The extent of tool wear from each pass is measured by using ImageJ software. Chips were collected after the five passes and were physically examined. The newly designed MQL delivery system was successful in delivering a fine mist of lubricant at the cutting point. MQL lubrication provided lower surface roughness and tool wear values compared to wet and dry conditions due to effective temperature reduction and improved lubrication penetration of cutting zones, as well as better chip flushing. Chip formed under MQL conditions had reduced chip thickness due to reduced temperatures. MQL was found to have the greatest positive impact on tool wear and surface roughness parameters based on ANOVA results.