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

Model Calibration and Feed Rate Scheduling

Journal of Manufacturing Science and Engineering (August 2002)

T. BaileyM. A. ElbestawiT. I. El-Wardany

P. Fitzpartick

Presented By:Levi Haupt

April 22, 2023ME 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 PaperMethodologyResultsConclusion

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 Regression4. Feed per tooth coefficients found from constant feed rate coefficient5. Average Coefficients found from tooth coefficients jajvaa

eiK321

5544332211

5544332211

5544332211

CKCKCKCKCKF

BKBKBKBKBKF

AKAKAKAKAKF

pz

py

px

Methodology5544332211

5544332211

5544332211

CKCKCKCKCKF

BKBKBKBKBKF

AKAKAKAKAKF

pz

py

px

Calibration Results:Solid line coefficient from step 4Third 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 StagesSurface 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)

References

Questions?

Happy Halloween!!