List of Symbols
2.5 Performance Parameters
2.5.2 Surface Finish
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exploited to attain better surface finish and reduced tool wear in single point diamond turning process.
Observations:
Analysis of machining forces is important in view of the computation of power consumption, machinability study, and prediction of tool failure. Till date, significant experimental works have been reported on understanding of the cutting mechanism and chip formation during machining of silicon and silicon carbide. However, as the machining scale enters from micro to nano regime, it becomes complicated and challenging to study the force, pressure, stress, and roughness during the cutting process. Important numerical research works on SPDT process using molecular dynamics (MD) have been reported to study the insight of the machining process by analyzing stress, chips formation, temperature, and phase change during the machining process. However, experimental validation of MD based results is still difficult due to its very small, i.e., atomic level study domain. Few attempts have been reported on numerical simulations using finite element method. Very scant literature is reported on the influence of various material models on the process performance during numerical simulation of SPDT process of brittle material. Selection of proper material model is essential in obtaining accurate results. Researchers have reported use of various material models such as Johnson-Cook, Johson-Homilquist and Drucker-Prager for modeling of ceramic materials. However, there is no comparative study amongst these material models for machining of Si and SiC is reported till date. Also, scant research work is reported on systematic parametric study based on the numerical simulation of SPDT of Si and SiC.
33 It is reported that, to achieve good surface finish, the brittle material should be machined in ductile mode. Many research works have been reported on experimental studies on SPDT of silicon and silicon carbide to study the ductile regime machining. Blake and Scattergood (1989, 1990) experimentally investigated the formation of ductile chips during machining of silicon and germanium. It was observed that ductile chips can be obtained if the depth of cut is maintained below 200 nm for germanium and 250 nm for silicon while using
−10º and −30º rake angle tool respectively. However, this thickness is affected by the tool rake angle. Lucca et al. (1994) studied the aspects of surface generation in orthogonal ultra- precision machining of copper using single point diamond tools. It was found that depth of damage was unaffected by cutting velocity, feed rate and nominal depth of cut whereas crystal orientation was seen to have largest affect. Leung et al. (1998) experimentally demonstrated the possibility of ductile-regime machining of single crystal silicon under different cutting conditions such as feed rate, depth of cut, tool rake angles, cutting lubricants and crystallographic orientation of the crystal being cut. It was reported that a surface finish in the order of 2.86 nm can be obtained at the process condition of speed of 10000 rpm, depth of cut of 1 μm, feed rate of 1 μm/rev, rake angle of −25º, clearance angle of 10º, tool nose radius of 0.637 mm and alcohol as cutting fluid. Cheung and Lee (2000) presented a model-based simulation system for the analysis of surface roughness generation in ultra-precision diamond turning. Later, Cheung and Lee (2001) presented parametric analysis of nano surface generation of aluminum alloy. It was found that the tool feed, tool geometry, spindle error motions and relative vibration between the tool, materials swelling and tool interference are the important factors that contribute to the surface generation in SPDT. Chan et al. (2001) investigated the surface generation in ultra-precision diamond turning of Al6061/15SiCp
metal-matrix composites based on different analytical approaches which include parametric analysis, cutting mechanic analysis, finite element method (FEM) analysis and power spectrum analysis. Parametric analysis was performed to explore the in-situ inter-relationships between the process parameters and the surface roughness. Khan et al. (2003) presented experimental study on Al6061 alloy to investigate the effect of feed rate on surface figure and surface finish keeping all other parameters constant. An empirical formula was derived from the practical surface finish versus the tool feed rate and observed that better quality surface finish can be obtained when a very low feed rate is employed. Bhattacharya et al. (2006) carried out scratching experiments of CVD coated silicon carbide using diamond styli and tool to study the ductile response and to determine the DBT depth. Results showed a surface roughness of less than 20 nm can be achieved with cutting speed of 0.001 mm/s, feed rate of 1μm/rev and estimated depth of cut of 500 nm and tool having 3 mm nose radius, −45º rake TH-2306_10610325
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angle and 5º clearance angle. Lee et al. (2007) developed a multiscale simulation model that comprises of a microplasticity model, a dynamic model and enhanced surface topography model. The model generates a 3D nanosurface and the simulated roughness values were found to agree well with the experimental ones. Arefin et al. (2007) conducted experiments on silicon to determine the upper bound of cutting edge radius. It was stated that for brittle material to be cut in ductile mode, the undeformed chip thickness must be smaller than the tool edge radius. However, in order to increase the productivity, the undeformed chip thickness is expected to be as large as possible. Therefore, the tool edge radius is expected to be as large as possible. The upper bound value reported in this work for cutting of silicon wafer material with a single crystal diamond tool was found to be between 700 to 800 nm.
Mohammadi et al. (2015) carried out diamond cutting of single crystal silicon coupled with micro-laser assisted machining (μ-LAM). A laser was used to heat and thermally soften the work piece material before carrying out the cutting process. Authors investigated the effect of laser heating and tool rake angle on the surface roughness. It was found that laser heating improves the surface roughness by 80% and −25º rake angle tool gives better surface finish than −45º rake angle tool.
Literature also reports eminent articles on prediction of surface roughness by developing mathematical expressions by using design of experiment (DOE) study of silicon [Krulewich (1996), Born and Goodman (2001)], silicon carbide [Patten and Jacob (2008), Ravindra and Patten (2008), Venkatachalam (2007)] and Al6061 [He et al. (2015), Khan et al.
(2003), Mishra et al. (2014)]. Yan et al. (2003) developed a ductile machining system based on the straight line enveloping method to fabricate convex axisymmetric aspheric surfaces on brittle materials. A surface was generated on single crystal silicon using a straight nosed diamond tool and average roughness value of 16 nm was successfully obtained. Chen and Zhao (2015) presented a surface roughness predicting model for Al7075 and evaluated the influence of relative tool-work vibration, machine tool error, cutting force, material property and cutting parameters on the surface roughness. He et al. (2015) presented a coupled theoretical and empirical method to predict the surface roughness achieved by single point diamond turning of hardening aluminum alloy. As per this method, the surface roughness was considered to be composed of both certain and uncertain parts. Radial basis function neural network and particle swarm optimization algorithm were employed to the experimental data to find out the optimal cutting parameters to achieve the best surface roughness.
Similarly, Khatri et al. (2015) conducted SPDT machining experiments on silicon by varying tool feed rate, spindle speed and depth of cut and studied the effect of machining parameters on the surface roughness by using response surface methodology (RSM). A prediction model
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35 was developed by using experimental data to predict the surface roughness. It was found that best surface roughness can be obtained on silicon was 31.6 nm at feed rate of 2.5 μm/rev, depth of cut of 1.5 μm and spindle speed of 1500 rpm.
Observations:
Surface roughness, an important product quality parameter affects several functional attributes of SPDT products viz. friction, wear, lubrication, light reflection/refraction, corrosion resistance. Theoretically, in case of the cutting process (turning or facing), the surface finish mainly depends on the tool nose radius and the feed rate. However, in reality, it depends upon the process parameters, tool geometry, workpiece and cutting tool interaction, workpiece and tool material properties, tool wear, cutting environment. As the surface roughness is measured after the completion of the machining operation, it is time-consuming and labor-intensive. Moreover, the SPDT process is slow as the volume of material removal is in micron or nanometric scale. Thus, carrying out extensive trials to optimize the process to obtain the desired surface finish is time-consuming. Literature reports that most of the works have been used either experimental or theoretical way to investigate the surface quality for SPDT process. There is hardly any attempt reported on modeling of surface roughness by using FEM simulations of SPDT process.