Performance-Based Predictive Models and Optimization Methods for Turning Operations and Applications: Part 3—Optimum Cutting Conditions and Selection of Cutting Tools

2007 ◽  
Vol 9 (1) ◽  
pp. 61-74 ◽  
Author(s):  
X. Wang ◽  
Z.J. Da ◽  
A.K. Balaji ◽  
I.S. Jawahir
Keyword(s):  
Predictive Models ◽  
Cutting Tools ◽  
Optimization Methods ◽  
Cutting Conditions ◽  
Turning Operations ◽  
Optimum Cutting ◽  
Selection Of

scholarly journals Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

2009 ◽  
Vol 22 (4) ◽  
pp. 491-504 ◽  
Author(s):  
Sukhomay Pal ◽  
P. Stephan Heyns ◽  
Burkhard H. Freyer ◽  
Nico J. Theron ◽  
Surjya K. Pal
Keyword(s):  
Tool Wear ◽  
Cutting Conditions ◽  
Tool Wear Monitoring ◽  
Optimum Cutting ◽  
Wear Monitoring ◽  
Input Uncertainties ◽  
Selection Of

scholarly journals Study on milling of super elasto-plastic β type titanium alloy (Verification of processing characteristics and selection of optimum cutting conditions)

2017 ◽  
Vol 83 (855) ◽  
pp. 17-00258-17-00258 ◽  
Author(s):  
Hideharu KATO ◽  
Kohei ITO ◽  
Akihiro KITAMURA ◽  
Noriaki IKENAGA ◽  
Kazuyuki KUBOTA
Keyword(s):  
Titanium Alloy ◽  
Cutting Conditions ◽  
Optimum Cutting ◽  
Selection Of

Optimization of milling operations for the selection of cutting conditions using Tribes

2005 ◽  
Vol 219 (10) ◽  
pp. 761-771 ◽  
Author(s):  
G C Onwubolu
Keyword(s):  
Performance Measures ◽  
End Milling ◽  
Parameter Tuning ◽  
Optimization Criterion ◽  
Cutting Conditions ◽  
Machining Performance ◽  
Milling Operations ◽  
Optimum Cutting ◽  
Selection Of

This paper presents a new methodology involving the use of Tribes for the selection of cutting conditions in single-pass milling operations. The new methodology, which is autonomous because it does not need any parameter tuning, is based on a comprehensive optimization criterion integrating the contributing effects of all major machining performance measures for milling operations. The results of case studies previously considered using genetic algorithms are presented to demonstrate the application of this new methodology for the determination of optimum cutting conditions in face- and end-milling operations. Results obtained show that the new methodology is efficient, effective, and competitive.

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