Optimizing Cutting Conditions for Minimum Surface Roughness in Face Milling of High Strength Steel Using Carbide Inserts
A full factorial design technique is used to investigate the effect of machining parameters, namely, spindle speed (N), depth of cut (ap), and table feed rate (Vf), on the obtained surface roughness (Ra and Rt) during face milling operation of high strength steel. A second-order regression model was...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/7372132 |
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| Summary: | A full factorial design technique is used to investigate the effect of machining parameters, namely, spindle speed (N), depth of cut (ap), and table feed rate (Vf), on the obtained surface roughness (Ra and Rt) during face milling operation of high strength steel. A second-order regression model was built using least squares method depending on the factorial design results to approximate a mathematical relationship between the surface roughness and the studied process parameters. Analysis of variance was conducted to estimate the significance of each factor and interaction with respect to the surface roughness. For Ra, the results show that spindle speed, depth of cut, and table feed rate have a significant effect on the surface roughness in both linear and quadratic terms. There is also an interaction between depth of cut and feed rate. It also appears that feed rate has the greatest effect on the data variation followed by depth of cut. For Rt, the results show that the table feed rate is the most effective factor followed by the depth of cut, while the spindle speed had a significant small effect only in its quadratic term. The conditions of minimum Ra and Rt are identified through least square optimization. Moreover, multiobjective optimization for minimizing Ra and maximizing metal removal rate Q is conducted and the results are presented. |
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| ISSN: | 1687-8434 1687-8442 |