Generic Simulation Approach for Multi-Axis Machining, Part 2:

1
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
• 22 March 2014
• ME 482

2
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

3
Outline

• Purpose of Paper
• Methodology
• Results
• Conclusion

4
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

5
Methodology

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

6
Methodology
Calibration Results Solid line coefficient from
step 4 Third graphs comparison of coefficients
7
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

8
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
9
Results
Approximately 30 reduction in machine time with
implemented feed rate schedule methodology
Feed Schedule 210 seconds
No feed schedule 293 seconds
10
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)

11
References
12
Questions?
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