Effect of process parameters on surface roughness produced by turning boring operation

Critical quality measure and surface roughness (Ra) in mechanical parts depends on turning parameters during the turning process. Researchers have predicted and developed various models to determine the optimum turning parameters for the required surface roughness. The main objective of this study i...

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Bibliographic Details
Main Author: Mohamed, Ahmad Dzia’Uddin
Format: Thesis
Language:English
Published: 2010
Subjects:
Online Access:http://eprints.utm.my/id/eprint/11241/1/AhmadDziauddinMohamedMFKM2010.pdf
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Summary:Critical quality measure and surface roughness (Ra) in mechanical parts depends on turning parameters during the turning process. Researchers have predicted and developed various models to determine the optimum turning parameters for the required surface roughness. The main objective of this study is to investigate the different cutting tool length parameter effect of surface roughness in CNC Turning boring operation for an aluminium 6061 workpiece. A fractional factorial design is use to evaluate the effect of five (5) independent variables (cutting speed, feed rate, depth of cut, tool length and diameter of boring bar) on the resulting first cut surface roughness (Ra). Vibration or chatter in internal turning operation is a frequent problem affecting the result of the machining and in particularly, the surface finish. This study found that using short tool length always produce a good surface roughness and that only slight improvement on surface roughness can be achieved by properly controlling the cutting parameters and/or the diameter size of boring bar. This study also found that using a long tool length may result in vibration that could be efficiently controlled by the use of larger diameter size of boring bar. With such a long tool length, the cutting variables become important factors to control in order to significantly improve surface roughness result with both diameter sizes of boring bars. A highly accurate prediction model for surface roughness is proposed for each types of boring bar.