generic simulation approach for multi-axis machining, part 2:
DESCRIPTION
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 30 July 2014 ME 482. Overview. - PowerPoint PPT PresentationTRANSCRIPT
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
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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?
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