Analysis and modeling of machining and surface integrity characteristics under various turning environments

Abstract

Aluminum alloys are widely used in aerospace components and aircraft and due to their high strength to weight ratio, good workability, and high resistance to corrosion. Surface integrity of Al alloys including surface roughness and residual stresses are dependent on machining characteristics including cutting temperature and machining forces in machining operations. Cutting fluids as coolant and/or lubrication can be utilized to affect the machining characteristics and possibly improve the part quality of components. This can increase fatigue and corrosion resistance of Al alloys. This research study was aimed at developing 2D and 3D finite element models to simulate machining characteristics and predict residual stresses in turning of AA6061-T6 for various environments. Moreover, another objective of the study was to conduct an experimental analysis, predictive regression modeling, and multi criteria optimization of residual stresses and surface roughness parameters under various turning environments. This research work is divided into three consecutive phases. First, a 2D finite element model was developed and experimentally validated for different machining parameters. The FE model was utilized to simulate the interactions between cutting edge radius and cutting speed, feed rate, and rake angle and investigate the influences of the above-mentioned tool geometry and cutting conditions on machining forces, cutting temperature, and chip thickness in orthogonal turning of AA6061-T6. Finally, the results of conventional machining (CM) and high speed machining (HSM) were compared. Secondly, turning tests were conducted using a Design of Experiment (DoE) based on Central Composite Design (CCD) under the three turning environments. The most efficient turning parameters were determined for each environment using Analysis of Variance (ANOVA). The impact of turning parameters including cutting speed, feed rate and depth of cut was investigated under DRY, MQL, and WET modes and their effect on surface roughness and residual stresses were analyzed. Response Surface Method (RSM) was used to predict effective regression models for each turning environment for the average arithmetic surface roughness, the height peak from the valley, the axial surface residual stress, and the hoop surface residual stress. Then, using the predictive regression models, a multi performance optimization study was carried out to identify optimal turning parameters in each environmental mode to improve surface integrity in Low Speed Turning (LST) and High Speed Turning (HST) of AA6061-T6. Finally, a 3D finite element model was developed to simulate and predict machining temperature (MT), machining forces (MFs), and axial surface residual stress (ASRS) for various turning environments and parameters. Turning environments consist of DRY, MQL, and WET modes, and turning parameters include both cutting conditions and tool geometry such as cutting speed, feed rate, depth of cut, tool nose radius, side cutting edge angle (SCEA), back rake angle (BRA). Special attention was devoted to simulating residual stresses in turning of AA6061-T6 alloys using the DEFORM-3DTM software. The 3D FE model was validated by comparing the predicted MFs and ASRS with the corresponding experimental measurements. The effect of cutting conditions including cutting speed, feed rate, depth of cut on axial residual stress was experimentally investigated for three turning environments and the influence of tool geometry consisting of tool nose radius, side cutting edge angle, and back rake angle was numerically studied using the 3D FE model of WET turning. The 2D/3D finite element and regression models can be utilized as predictive reliable tools for industrial applications to improve and optimize machining and surface integrity characteristics for various turning environments and parameters.

Date	23 Nov 2020
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Victor Songmene (Supervisor) & Mohammad Jahazi (Co-supervisor)