Numerical Modeling and Experimental Validation in Orthogonal Machining of Aluminum Al 6061-T6 Alloy

Proper selection of cutting parameters, cutting tool material and geometry and machine tool selection is necessary for the production of high-quality products at reduced cost. Cutting forces produced during the machining process are important indicators of the cutting mechanism. The knowledge of the cutting forces during a machining operation helps to select a workpiece of a suitable strength in order to maintain dimensional tolerances by avoiding excessive distortions. Machining is one of the most common manufacturing operations today. A number of research works have been conducted in the past to quantify cutting forces experimentally and numerically during machining processes because theoretical calculations appeared to produce uncertain results due to complex workpiece and tool interaction and inherent complexity of machining process. The numerical analyses have been continuously improved for the prediction of the fundamental physical quantities. However, a general predictive model that can capture the real cutting operation is not available yet due to the presence of extremely complex phenomena associated with the actual cutting operation including tool-chip friction, adiabatic shear bands, free surfaces, high strains and strain rates and high temperatures etc.The objective of this research was to investigate the use of Johnson-Cook material model in simulating orthogonal cutting of Al 6061-T6 alloy. The idea in the current research was to develop a more economical solution to the existing dynamometers which are highly expensive. A cost-effective strain gauge based (mechanically decoupled, beam type static) dynamometer has been designed, developed and tested for finding the cutting forces during orthogonal machining operation which was not considered in the past research studies.Results of force variations measured experimentally through strain gauge based dynamometer as well as predicted numerically through simulation were compared with the published results during machining of Aluminum alloy Al 6061-T6 and found in good agreement.


INTRODUCTION
I n the machining process, a piece of raw material is machined into a desired final shape and size by a controlled material-removal process [1]. Machining processes are used to achieve the desired shape,   The factor of safety was considered to be "4".

Structure Optimization
For structure optimization, the following Equations (1-3) [22] were used to select an appropriate cross-section of the elastic beams. For a beam subjected to a moment force, the maximum stress is defined by the term Flexural stress and is given by Equation (1) where 'm' is the moment, 'c' is the distance from the neutral axis to the edge of the beam, 'I' is the moment of inertia, 'b' is the width of the section of the cantilever beam (for designed structure, b = 8mm) and 't' is the thickness of the section of the cantilever beam (for designed structure, t = 13mm).

EXPERIMENTATION
The experimental work was carried out in SMME (School

Material Model
Much of the difficulty to accurately model a machining The J-C plasticity model is a function of strain, strain rate and temperature. The expression in the first set of brackets shows the strain effect, the second set of bracket shows the effect of strain-rate on yield-strength of the material whereas the last expression in the third bracket shows the thermal softening effect on the yield strength. It should be noted that yield stress drops to zero as the temperature reaches to the melting point. The constant values for Al 6061-T6 alloy that is used in the finite element analysis are given in Table 4 and the corresponding mechanical properties are given in Table 5.

Damage Model
The J-C damage model is suitable where materials deform with high strain-rates [43]. The equivalent plastic strain is obtained at element integration points and failure is assumed to occur when the damage parameter exceeds one. When stress and deformation states in a small region ahead of tooltip satisfy damage initiation criteria, material starts to deform producing chips. J-C damage model is given by Equation (5) [26][27]39].
Where, equivalent plastic strain, ε f is given by Equation (6) [6,27,33,35]: The three terms in the above equation represent pressure dependency, strain-rate effect and the thermal effect, respectively. The values of five damage constants, D1-5 for Al 6061-T6 alloy is given in Table 6.

Friction Model
Friction occurs between rake face and the chip. It is one of the most complex phenomena in metal cutting operation and affects the variation in the cutting force, surface finish, tool wear and chip morphology etc. [45]. Friction can be generally considered as the tangential force generated between the two surfaces. In the current research work, the most simple friction model i.e. Columb's friction model is used. The present work assumes a constant value of the coefficient of friction as 0.25 [6,[46][47]

Modelling, Boundary Conditions and Loading
Since the temperature generated during the machining operation significantly affect many parameters including chip morphology, cutting forces, residual stresses [48][49][50] and tool wear [51][52][53] it is important to develop a model that can capture the temperature effects. Both the workpiece and tool with 0o rake angleand 7o clearance angle has been kept deformable during the simulation of orthogonal machining. Fig. 8   Choice of the element is an important criterion to obtain better predicted of results. Performance of elements with low order shape functions is poor, however, they are usually preferred due to their inherent simplicity especially in cutting simulations where many complicated processes may be occurring simultaneously [54]. As Fig. 8

Cutting Conditions for Modeling
Simulations for the 12 different cutting conditions were carried out to validate experimental and published results.
The length of the workpiece material was set to be 5 mm whereas; the thickness was kept at 2mm. The workpiece material was constrained both in x-y directions at the bottom of the workpiece whereas, the tool was given prescribed velocity on in x-direction as shown in Fig. 8.
Cutting force, Fc generated for different cutting speeds and feed rates were determined by keeping the depth of cut, ap and tool geometry constant. These forces were then compared with the published and experimental results of the current research work using a strain-gauge based dynamometer in order to validate the current Finite Element model.