CHAPTER 3 SURFACE ROUGHNESS

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1 38 CHAPTER 3 SURFACE ROUGHNESS 3.1 SURFACE ROUGHNESS AND ITS IMPORTANCE The evaluation of surface roughness of machined parts using a direct contact method has limited flexibility in handling the different geometrical parts to be measured. Surface roughness also affects several functional attributes of parts, such as friction, wear and tear, light reflection, heat transmission, ability of distributing and holding a lubricant, coating etc. Therefore, the desired surface finish is usually specified and appropriate processes are required to maintain the quality. Hence, the inspection of surface roughness of the work piece is very important to assess the quality of a component. Alternately, optical measuring methods are applied to overcome the limitations of stylus method, but, they are also sensitive to lighting conditions and noise. The technique proposed in this work, requires no apriority information about the lighting conditions and source of noise. It is important that the specification should include a maximum transverse surface roughness requirement. Scanning Electron Microscopy (SEM) revealed that the samples with a high surface roughness are heavily damaged by the polishing operation, whilst those with low surface roughness are relatively undamaged, showing only light scoring of the surface. Mean values of selected roughness parameters for common technological process are shown in Table 3.1.

2 39 Table 3.1 Mean value of roughness parameters Machine Roughness Parameters Ra[µm] Rz[µm] RSm[µm] Rλq[µm] R q[*] T 0.27± ± ± ± ±0.64 BG ± ± ± ± ±0.28 BG ± ± ± ± ±0.04 It can be observed from Table 3.1 that the mean values and confidence intervals of vertical and hybrid parameters values are decreasing with every step of technological process. However, the horizontal parameters change their values in a different way. 3.2 SURFACE ROUGHNESS PARAMETER (Ga) An important feature of surface image that can be used to predict surface roughness is arithmetic average of gray levels, Ga. This parameter (Ga) is expressed in eqn. (3.1) where, g1,g2,g3,, gn are the gray level values of a surface image along one line and gm is the mean of the gray values as given in eqn.(3.2) Quantification methods based on 2D parameters are well described by IS0 standards and by far are the most widely used. The description of the roughness measure is shown in figure 3.1

3 40 Figure 3.1 Roughness measures 3.3 MACHINE VISION BASED IMAGE PROCESSING Image processed using a computer is digitized first, after which it may be represented by a rectangular matrix with elements corresponding to the brightness at appropriate image locations using machine vision approach. From the image vector, a rough surface can be understood as an image with the grey levels corresponding to the surface relief and deeper a valley, the darker the corresponding pixel. Similarly, the higher a peak is the brighter the corresponding area in the image. Texture analysis of these images (in order to characterize them) is still an open field, as there is no single technique that can be used to entirely characterize a texture. The Form waviness and roughness decomposition of a surface texture profile obtained by vertical milling is shown in Figure 3.2.

4 41 Figure 3.2 Form waviness and roughness decomposition of a surface texture profile obtained by vertical milling Most of the texture analysis schemes shared a common weakness of analyzing an image at one single-scale, a limitation that can be lifted by employing a multiscale representation of the textures by using the wavelet transform. On the other hand, most of the usual characterization techniques that are especially useful for space stationary signals can be a drawback when analyzing non-stationary signals (case very often with surface textures). An effective way to analyze such non-stationary signals could be to use spacefrequency methods Specification of Milling Operation Experiments with different operating conditions (i.e., varying speed, feed, and depth of cut) are conducted on a milling process. The machining parameters used for milling are given in Table 3.2. The specifications of the milling machine

5 42 are given in Table 3.3, tool specifications in Table 3.4 and composition of work piece in Table 3.5. Table 3.2 Machining Parameters for milling Tool Speed Carbide Feed (m/sec) (mm/min) Depth of cut Cutter (mm) diameter (mm) Table 3.3 Specifications of milling machine Mm Type Longitudinal 630 Cross feed 250 Height 450 Table 3.4 Tool specification Tool type Mm Tool Diameter 25φ Table 3.5 Composition of work piece Material Grade C Si Mn Co EN8 SAE 0.35% 0.10% 0.6% 0.06% 1038

6 VARIATION OF SURFACE ROUGHNESS WITH MACHINING PARAMETERS FOR MILLING The variation of the surface roughness parameter R t with the machining conditions V, F and D for milling process is given in Table 3.6.The Rt value is obtained using the standard stylus method. Quantitative characterization of surface can be grouped in: 2D parameters, 3D parameters, parameters based on integral methods, fractals, wavelets and others. A 3D power spectra of a machined surface along with the Rt value is shown in Figure 3.3. Table 3.6 Machining conditions for milling process Machining Conditions V(m/s) F(m/rev) D(mm) Stylus Rt(µm)

7 44 Figure 3.3 Surface image of milled surface with Rt = µm (a) Power spectra in 3D (b) Power spectra displayed as an intensity function 3.5 ROUGHNESS ESTIMATION OF MILLED COMPONENTS Typical Milling Images Typical images of the surface texture for milling along with their histogram are shown in Figure 3.4. Figure 3.4 Typical Milled images with histogram

8 45 As inferred from Figure 3.4 there is a shift of the histogram towards higher grey levels as the surface becomes smoother i.e. the reflectivity increases, resulting in higher grey level values. The study of correlation between the gray levels and the surface roughness values provides a machine independent way of estimating the surface roughness. 3.6 PREDICTION MODEL The prediction and regression analysis uses a set of tested methods that would analyze the data and distinguish patterns. This thesis proposes a nonprobabilistic binary linear model i.e. it predicts, for each given element of inputs, the surface roughness parameter. The task generally involves with training and testing data consisting of some data instances. Each instance in the training set contains one target value (class labels) and several attributes (features). The present prediction has an additional advantage of automatic model selection in the sense that both the optimal number and locations of the basis functions are automatically obtained during training. For the input training sample set the prediction hyperplane equation is let to be thus the prediction margin is 2 / ω.to maximize the margin, that is to minimize ω the optimal hyperplane problem is transformed to quadratic programming problem as follows,

9 46 After introduction of Lagrange multiplier, the dual problem is given by, According to Kuhn-Tucker rules, the optimal solution must satisfy That is to say if the optimal solution is Then For every training sample point, there is a corresponding Lagrange multiplier. And the sample points that are corresponding to don t contribute to solve the prediction hyperplane while the other points that are corresponding to do, so it is called support vectors. Hence the optimal hyperplane equation is given by, The hard predictor is then,

10 47 For nonlinear situation, it is required to construct an optimal separating hyperplane in the high dimensional function space by introducing kernel ence the nonlinear predictor is given by, And its dual problem is given by, Thus, the optimal hyperplane equation is determined by the solution of the optimal problem. 3.7 SURFACE ROUGHNESS EVALUATION The surface roughness related parameters are to be computed as a basic prerequisite of the experiment. In order to remove artifacts from the measured sensor values Wavelet based novel filter is used, and a predictor (multi objective) based on the filtered surface roughness related features coexist as shown in Figure 3.5. This integrates the predictor output and performs surface roughness estimation. Figure 3.5 Block diagram of Surface Roughness Evaluation

11 48 A filtering process is to be used to establish a three-dimensional reference surface consisting of waviness and form errors and the roughness component needs to be separated with reference to it. The Wavelet technique in this work, used for profile/surface analysis, exploits the waviness transmission characteristics through the wavelet decomposition filters and the feature extraction technique does not contain any machined parameter component. This differs from previous reported works, where the feature extraction technique contains some transformed features along with machined parameters. Thus, the advantage of the presented feature extraction technique is that, it can be adapted generically in any machine vision applications. 3.8 DESIRED FEATURES FOR NEW IMAGE REPRESENTATION 1. Multiresolution-successive refinement. 2. Localization-Both space and frequency. 3. Critical Sampling-correct joint sampling. 4. Directionality-More directions. 5. Anisotropy-More shapes. The emphasis is therefore on discrete framework that leads to computationally less intensive algorithmic implementations. 3.9 NOISE REMOVAL SCHEMES The noise elimination strategies suited for noise cancellation in a real time environment favours a noise cancellation model in which the secondary noise source n2(k) is absent, while retaining the constraints that (i) x(k) and n1(k) can be non-stationary and (ii) no a priori statistical information is required about x(k) and n1(k) Proposed Noise Removal filter (NRF) and its Advantages This network architecture is shown in Figure 3.6. The delay path serves to generate a reference signal, that can be used to estimate x(k). No a priori

12 49 statistical information about the desired signal is required to be given as input to the network. Also, the source signal x(k) need not be stationary. Figure 3.6 Adaptive noise cancellation networks for non-stationary signals with no secondary noise source 3.10 CHAPTER CONCLUSION In this chapter, the importance of evaluating the surface roughness measure is discussed. The specifications of milling and grinding were also presented. The proposed image enhancement scheme has the advantage that it is independent of the frequency band in which the noise affects the image.

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