Identification of instabilities of the chip formation and it’s prediction model during end milling of medium carbon steel (S45C)
Problem statement: Chip shape and size varied widely in machining operations. Undesirable chip formation had a detrimental effect on surface finish, work-piece accuracy, chatter and tool life. Approach: This study included the findings of an experimental study on the instabilities of the chip format...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Science Publications
2010
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/16686/ http://irep.iium.edu.my/16686/ http://irep.iium.edu.my/16686/ http://irep.iium.edu.my/16686/1/ajeas31193-200%5B1%5D.pdf |
| Summary: | Problem statement: Chip shape and size varied widely in machining operations. Undesirable chip formation had a detrimental effect on surface finish, work-piece accuracy, chatter and tool life. Approach: This study included the findings of an experimental study on the instabilities of the chip formation and development of a mathematical model based on statistical approach for the prediction of the instability of chip formation during the machining of medium carbon steel (S45C). Results: It has been identified that the chip formation process has a discrete nature, associated with the periodic shearing process of the chip. Typical instabilities of periodic nature, in the form of primary and secondary saw/serrated teeth, which appear at the main body and free edge of the chip respectively, have been identified. Mechanisms of formation of these teeth have been studied and the frequencies of their formation have been determined under various machining conditions. Small Central composite design was employed in developing the chip serration frequency model in relation to primary cutting parameters by Response Surface Methodology (RSM). Conclusion/Recommendations: The mathematical model for the chip serration frequency has been developed, in terms input cutting parameters (cutting speed, feed and depth of cut) in end milling of S45C steel using TiN inserts under full immersion. The adequacy of the predictive model was verified using ANOVA at 95% confidence level. |
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