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Fig 3.2: Dimensional representation for the parameters levels experiment, and selection of the each circle is random.

Figure 3 2: Dimensional representation for the parameters levels experiment, and selection of the each circle is random.

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TABLE OF ABBREVIATIONS
TABLE OF ABBREVIATIONS
Fig 2.1: Principal Components of a central lathe form of chips [13]. Fig.2.1 shows the working principle of lathe. Lathe is one of the most versatile and widely used machine tools all
Fig 2.1: Principal Components of a central lathe form of chips [13]. Fig.2.1 shows the working principle of lathe. Lathe is one of the most versatile and widely used machine tools all
Fig 2.2: Different form of deviations of surfaces To achieve high dimensional accuracy, the machining process must
Fig 2.2: Different form of deviations of surfaces To achieve high dimensional accuracy, the machining process must
Fig 2.5 Illustration of ten spot average roughness added together. Rz is this sum, in microns (um) [16]. 2.4 Important parameters of cutting process The process of cutting parts in turning machine has many affecting
Fig 2.5 Illustration of ten spot average roughness added together. Rz is this sum, in microns (um) [16]. 2.4 Important parameters of cutting process The process of cutting parts in turning machine has many affecting
neurons output n(i). The various n(i) taken together form an S-element net input 2.5.3.1 The one-layer network with R input elements and S
neurons output n(i). The various n(i) taken together form an S-element net input 2.5.3.1 The one-layer network with R input elements and S
Fig 2.7: Abbreviated notation of one-layer of neurons
Fig 2.7: Abbreviated notation of one-layer of neurons
Fig 2.8: Multiple Layers of Neurons
Fig 2.8: Multiple Layers of Neurons
Fig 2.9: three-layer network drawn by abbreviated notat 2.5.4 The come types of transfer functions ising our abbreviated notation [19]. Fig 2.9: three-layer network drawn by abbreviated notation The same three-layer network discussed previously also can be drawr
Fig 2.9: three-layer network drawn by abbreviated notat 2.5.4 The come types of transfer functions ising our abbreviated notation [19]. Fig 2.9: three-layer network drawn by abbreviated notation The same three-layer network discussed previously also can be drawr
Fig 2.13: Response surface plot 2.6.2 Response Surface Methods and Designs
Fig 2.13: Response surface plot 2.6.2 Response Surface Methods and Designs
Fig 2.14: Contour plot are essential for analyzing the unknown function f [20].
Fig 2.14: Contour plot are essential for analyzing the unknown function f [20].
Table 3.1: Process parameters with different operating levels And then 27 experiment will been done in work-piece that we want
Table 3.1: Process parameters with different operating levels And then 27 experiment will been done in work-piece that we want
Fig 3.4: Tester surftest (SJ-210) series
Fig 3.4: Tester surftest (SJ-210) series
using for turning is (CVD)-coated carbide insert. 3.4 Surface roughness measurement experiments are completed, the length of section is 15 mm, the tool bit the experiments will assumed to be constant. Each work-piece is dividec
using for turning is (CVD)-coated carbide insert. 3.4 Surface roughness measurement experiments are completed, the length of section is 15 mm, the tool bit the experiments will assumed to be constant. Each work-piece is dividec
Fig. 3.7: Mathematical principal of a neuron its output activation. An activation function consists of linear and nonlineat
Fig. 3.7: Mathematical principal of a neuron its output activation. An activation function consists of linear and nonlineat
output and the network output is known as error. network output is compared to the target. The difference between the target
output and the network output is known as error. network output is compared to the target. The difference between the target
Table 4.2: ANOVA resul
Table 4.2: ANOVA resul
Fig 4.1: 3D surface graphs for surface roughness and finally between feed rate and depth of cut shown in Fig 4.2 (C).
Fig 4.1: 3D surface graphs for surface roughness and finally between feed rate and depth of cut shown in Fig 4.2 (C).
4.5 Main effects plot Figure 4.3 presents the main effect plots for surface roughness
4.5 Main effects plot Figure 4.3 presents the main effect plots for surface roughness
highest inclination will have the most significant effect. Fig 4.3: Main effects plot for (Ra) cutting parameters
highest inclination will have the most significant effect. Fig 4.3: Main effects plot for (Ra) cutting parameters
Fig 4.4: Target vs output of the (ANN) model
Fig 4.4: Target vs output of the (ANN) model
The mean square error in the (ANN) model was found to be 0.0016. Fig 4.8: The comparison of predicted and actual values of surface roughness
The mean square error in the (ANN) model was found to be 0.0016. Fig 4.8: The comparison of predicted and actual values of surface roughness
Fig 4.9: Graphical representation for the errors of ANN and RSM model Table 4.4: Comparison of ANN and RSM model studying parameters are represent the physical phenomena well or else. perform statistical tests (i.e. ANOVA) which determine whether the
Fig 4.9: Graphical representation for the errors of ANN and RSM model Table 4.4: Comparison of ANN and RSM model studying parameters are represent the physical phenomena well or else. perform statistical tests (i.e. ANOVA) which determine whether the
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Hint: ¢
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