Generic Simulation Approach for Multi-Axis Machining, Part 2: Model Calibration and Feed Rate Scheduling Journal of Manufacturing Science and Engineering (August 2002) T. Bailey M. A. Elbestawi T. I. El-Wardany P. Fitzpartick Presented By: Levi Haupt 13 July 2015 ME 482

Overview Development of Least-Squares Fit model of multi-axis machining Process optimization for specified parameters Determine instantaneous feed rate based on load prediction model Improve machining time while maintaining geometric specifications

Outline Purpose of Paper Methodology Results Conclusion

Purpose of Paper Develop methodology to simulate machining of complex surfaces (Calibrating Coefficients) Validate simulated results with experimental data Demonstrate results through airfoil case study

Methodology Model Calibration  Calibration of cutting for coefficients 1.Formulate force model to separate cutting force coefficients and geometric factors 2.Cutting test performed varying cutting speed, feed rate, axial depth of cut, radial width of cut, from test cutting force data obtained 3.Coefficients found from test data utilizing Least-Square Fit Regression 4.Feed per tooth coefficients found from constant feed rate coefficient 5.Average Coefficients found from tooth coefficients

Methodology Calibration Results: Solid line coefficient from step 4 Third graphs comparison of coefficients

Methodology Feed Rate Scheduling  Variation of feed rate will prevent tool damage (chatter) and improve surface finish Productivity traditionally decreased to improve process parameters  Process constraints: shank and tooth breakage, chatter limits, surface dimensional error Utilizing chip load or force constraints will satisfy all other constraints –Roughing: Max force constraint –Finishing: Max chip load constraint Feed rate determined for instantaneous cut geometry and forces based on constraints

Results Average Coefficient model predictions of loads within 5% of experimental data Case Study: “Airfoil like surface”  19mm solid carbide ball end mill  30 roughing passes Roughing Stages Surface Profile

Results Feed Schedule: 210 secondsNo feed schedule: 293 seconds Approximately 30% reduction in machine time with implemented feed rate schedule methodology

Conclusion Successful analytical model  Demonstrated accurate force predictions Within 5% of experimental data  Versatile for complex machining surfaces  Can also be used to predict static and dynamic tool deflections, dynamic cutting forces Technical Advancements  Improved model accuracy  Improve feed rate scheduling 30% reduction of machining time  Methodology implemented in industry Possible limitations  Excitation of dynamics in feed rate controller system Parallel research was conducted (source 17)

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