Response surface methodology approach for chip serration Frequency Prediction in milling

• Main Authors: Patwari, Muhammed Anayet Ullah, Amin, A. K. M. Nurul, Faris, Waleed Fekry
• Format: Article
• Language: English
• Published: Springer 2008
• Subjects: TS Manufactures
• Online Access: http://irep.iium.edu.my/17329/2/Advanced_Manufacring_process-second_review.pdf; http://irep.iium.edu.my/17329/; http://www.springer.com

Chip morphology and segmentation play a predominant role in determining Machinability and Chatter during the machining operation of materials. The chip shape and size varies widely in machining operations. Problems with surface finish, work-piece accuracy, chatter and tool life can be caused even due to minor changes in the chip formation process, especially in high speed machining, where undesirable chip formation will have a more detrimental effect. This paper includes the findings of an experimental study on the instabilities of the chip formation and development of a mathematical model for the prediction of the instability. 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. This paper also presents a statistical approach based on response surface methodology (RSM) for the prediction of secondary chip segmentation frequency. The mathematical model for secondary chip serration frequency has been developed, in terms input cutting parameters in end milling of Ti6Al4V alloy using TiN inserts under full immersion. Central composite design was employed in developing the chip serration frequency models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The adequacy of the predictive model was verified using ANOVA at 95% confidence level.