CASE STUDY

**6.1 The Description of problem**

- Depth of cut
- Cutting Speed
- Width of cut
- Shear angle
- Rake angle
- Flank angle
- Cutting Force
- Thrust Force
- Convection coefficient of coolant
- Convection coefficient of air
- Ambient temperature
- Thickness of tool coating
- Material

*Figure 6.1 Design variables that affect the heat
flow into the workpiece
*

The finite element model of the above machining process consists of 2-D thermal solid elements (Stiff 55 of ANSYS Software). This element type is used for conducting the steady state thermal analysis with conduction capability. In addition to this the heat generation within the body and convection along the edges can be simulated. From the heat flux calculations, it is possible to find out the total heat flow into the workpiece. The partial listing of ANSYS parametric input file for the above model, called metcut.log, has been given in the Appendix B. The meshed model of the machining process is shown in Figure 6.2. Every time the experiment is conducted, the geometry automatically gets changed depending on the level settings of the design variables.

**6.4 Conducting the experiments**

The following are the steps involved in the experimental design of machining process.

*Figure 6.3 Object oriented black board database
*

**6.4.2 Selection of the independent variables**

*Figure 6.4 Selection of independent design variables
*

**6.4.3 Selection of the Objective function**

The objective function is the post-processing item of ANSYS software. In case the objective function is not directly expressible by a single equation, a set of ANSYS command may be necessary to obtain the objective function value. For the present case, the heat flow into the workpiece calculated separately and is stored in a separate file. The file, post.ans has been used for this purpose which is listed in Appendix C.

**6.4.4 Conducting the Taguchi Experiment**

Once the experiments are over, the program calculates
the mean value of each level of all the variables, the sum of
squares, the percent contribution and the near optimum level of
each design variable. Based on the near optimum level of each
variable, a new experiment is conducted with each design variable
set to the near optimum level value. The program also calculates
the F-value for each design variable. The summary of experimental
results are shown in Table 6.1. The detailed output listing is
given in Appendix D.

Variable Name | ||||||||

F-Value | F(2,15,.90) | Whether Significant ? | ||||||

1. To (degree) | ||||||||

2. T_force (N) | ||||||||

3. Cut_Force (N) | ||||||||

4. Rake_ang(deg) | ||||||||

5. Shearang(deg) | ||||||||

6. C_width(inch) | ||||||||

7. D_cut (inch) |

It can be observed from the analysis results that the most significant
factor which dominates the heat flow into the workpiece is the
depth of cut. The other two factors viz. cutting force and shear
angle contribute to a limited extend. Though the percent contribution
of these two variables are significant, the ANOVA test conducted
at 90% confidence level rejects the significance of these design
variables.

*Figure 6.5 Effect of different level values on the objective
function
*

*Figure 6.6 Percent contribution of each design variable*

*Figure 6.7 Experimental results and optimum level values*

**6.6 Comparison of local optimum results **

Experiment Type | CPU time
(Seconds) | Optimum level values | Optimum performance | Remarks |

1. Based on Taguchi method | 1.Cut_force = 50
2.D_cut = .003 3.C_width=.08 4.Rake_ang=2 5.Shearang=40 6.T_force=40 7.To = 30 | For near optimum
solution only | ||

2. Standard optimization
technique |
1.cut_force = 50
2.D_cut = .003 3.C_width=.078 4.Rake_ang=4 5.Shearang=40 6.T_force=31.399 7.To = 25 | Conduct the standard optimization without conducting the design of experiments | ||

3. Taguchi method +
Standard optimization technique | 1.Cut_forc = 50
2.Shearangle=40 3.D_cut = 0.003 | Use design of experiments results as an initial step. (use only significant design parameters) |