Abstract
ABSTRACT In the process of milling titanium alloy, workpiece surface roughness is mainly affected by cutting parameters, tool angle, tool shape and system vibration. There is a complex highly nonlinear relationship among these factors, which makes it impossible to build an accurate mathematical model. Therefore, an effective surface roughness prediction method is of great significance to improve machining efficiency and reduce machining cost. In this paper, taking flank milling Ti6Al4 V alloy as an example, a surface roughness prediction framework (SRPF) based on CLBAS-BP algorithm is proposed. Chaotic Lorentz system and Lévy flight strategy are used to optimize BAS algorithm, which can improve local search ability, solution precision and convergence speed. CLBAS-BP algorithm has higher prediction accuracy than other algorithms, and can predict workpiece surface roughness with various cutting parameters. This study provides the technique foundation for improving the high-precision manufacturing of products.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computer Integrated Manufacturing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
