DEVELOPMENT OF EMPIRICAL MODEL FOR PREDICTION OF SURFACE ROUGHNESS USING REGRESSION
In this present work, the important challenge is to manufacture high quality and low cost products within the stipulated time. The quality is one of the major factors of the product which depends upon the surface roughness and hence the surface roughness placed an important role in product manufacturing. Hence, an Empirical model is proposed for prediction of surface roughness in machining processes at given cutting conditions. The model considers the following working parameters spindle speed, feed, depth of cut, number of flutes and overhang of the tool. For a given work-tool combination, the range of cutting conditions are selected from different cutting condition variables. The experiments were conducted based on the principle of Factorial Design of Experiment (DOE) method with mixed level. After conducting experiments, surface roughness values are measured. Then these experimental results are used to develop an Empirical model for prediction of surface roughness by using Multiple Regression method. In this the Artificial Intelligence based neural network modelling approach is presented for the prediction of surface roughness of Aluminium Alloy products machined on CNC End Milling using High speed steel tool. Trails were made with different combinations of step size and momentum to select the best learning parameter. The best network structure with least Mean Square Error (MSE) was selected among the several networks. The multiple regression models, which are most widely used as prediction methods, are considered to be compared with the developed Artificial Neural Network (ANN) model performance.
In this present work, the important challenge is to manufacture high quality and low cost products within the stipulated time. The quality is one of the major factors of the product which depends upon the surface roughness and hence the surface roughness placed an important role in product manufacturing. Hence, an Empirical model is proposed for prediction of surface roughness in machining processes at given cutting conditions. The model considers the following working parameters spindle speed, feed, depth of cut, number of flutes and overhang of the tool. For a given work-tool combination, the range of cutting conditions are selected from different cutting condition variables. The experiments were conducted based on the principle of Factorial Design of Experiment (DOE) method with mixed level. After conducting experiments, surface roughness values are measured. Then these experimental results are used to develop an Empirical model for prediction of surface roughness by using Multiple Regression method. In this the Artificial Intelligence based neural network modelling approach is presented for the prediction of surface roughness of Aluminium Alloy products machined on CNC End Milling using High speed steel tool. Trails were made with different combinations of step size and momentum to select the best learning parameter. The best network structure with least Mean Square Error (MSE) was selected among the several networks. The multiple regression models, which are most widely used as prediction methods, are considered to be compared with the developed Artificial Neural Network (ANN) model performance.
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International Journal of Advances in Engineering & Technology, Nov. 2013.<br />
©IJAET ISSN: 22311963<br />
Table 10. Experimental values and Predicted values (Test Data)<br />
Expt. No. V(rpm) F(mm/min) D(mm) NF OL<br />
(mm)<br />
Ra(mea) ANN Ra Second Order<br />
Multiple<br />
Regression Ra<br />
1 2200 220 0.55 2 32 3.68 3.685107 2.7396<br />
2 2200 235 0.55 2 32 3.75 3.660814 2.9631<br />
3 2600 180 0.55 2 32 3.28 3.327078 2.1628<br />
4 2600 180 0.75 2 32 2.87 3.004944 2.1628<br />
5 2600 220 0.55 2 32 4.08 3.979221 2.7588<br />
6 2600 220 0.75 2 32 3.71 6.472651 2.7588<br />
7 2600 235 0.55 2 32 4.4 4.122594 2.9823<br />
8 2600 235 0.75 2 32 4.11 4.091136 2.9823<br />
9 2600 180 0.75 4 32 2.47 2.320524 2.7356<br />
10 2600 220 0.55 4 32 3.18 3.316616 3.3316<br />
11 2600 235 0.55 4 32 3.42 13.091 3.5551<br />
12 2900 180 0.55 4 32 2.61 2.353685 2.2604<br />
13 2900 180 0.75 4 32 2.48 2.515347 2.2604<br />
14 2900 220 0.55 4 32 3.53 3.439947 2.8564<br />
Percentage Deviation 4.402905 20.2564<br />
Surface Roughness<br />
8<br />
7<br />
6<br />
5<br />
4<br />
3<br />
2<br />
1<br />
0<br />
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46<br />
Number Of Experiments<br />
Exp Ra<br />
ANN Ra<br />
MRE 2 Ra<br />
Figure 2. Experimental and predicted Ra Values (Train Data)<br />
Surface Roughness<br />
5<br />
4.5<br />
4<br />
3.5<br />
3<br />
2.5<br />
2<br />
1.5<br />
1<br />
0.5<br />
0<br />
1 2 3 4 5 6 7 8 9 10 11 12 13 14<br />
Exp Ra<br />
ANN Ra<br />
MRE 2 Ra<br />
Number Of Experiments<br />
Figure 3. Experimental and Predicted Ra Values (Test Data)<br />
VII.<br />
CONCLUSION<br />
This paper focuses on developing a model for surface roughness prediction in CNC End-milling<br />
machine. The experiments are conducted by using Design of Experiments with mixed level. These<br />
experiments are conducted on Aluminum alloy of H30 grade using High Speed Steel tool. For the<br />
development of surface roughness prediction model, two competing modelling techniques, multiple<br />
regression and artificial neural networks, are considered.<br />
2050 Vol. 6, Issue 5, pp. 2041-2052
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