Surface roughness prediction in turning of femoral head

This paper is dealing with the development of a surface roughness model for turning of femoral heads from AISI 316L stainless steel. The model is developed in terms of cutting speed, feed rate and depth of cut, using response surface methodology. Machining tests were carried out with TiN–Al 2 O 3 –T...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2010-11, Vol.51 (1-4), p.79-86
Main Authors: Galanis, Nikolaos I., Manolakos, Dimitrios E.
Format: Article
Language:English
Subjects:
Aluminum oxide
Austenitic stainless steels
CAE) and Design
Carbide cutting tools
Carbide tools
Computer-Aided Engineering (CAD
Cutting parameters
Cutting speed
Cutting tools
Engineering
Feed rate
Industrial and Production Engineering
Mechanical Engineering
Media Management
Original Article
Predictions
Production planning
Response surface methodology
Statistical methods
Surface roughness
Test procedures
Titanium carbide
Turning (machining)
Variance analysis
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Summary:This paper is dealing with the development of a surface roughness model for turning of femoral heads from AISI 316L stainless steel. The model is developed in terms of cutting speed, feed rate and depth of cut, using response surface methodology. Machining tests were carried out with TiN–Al 2 O 3 –TiC-coated carbide cutting tools under various conditions. First-order and second-order models predicting equations for surface roughness have been established by using the experimental results. The established equation shows that the depth of cut was the main influencing factor on the surface roughness. It increased with increasing the depth of cut and feed rate, respectively, but it decreased with increasing the cutting speed. In addition, analysis of variance for the second-order model shows that the interaction terms and the square terms are statistically insignificant. The predicted surface roughness of the samples was found close to the experimentally obtained results within a 95% confident interval.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-010-2616-4