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TECHNICAL PAPERS

Model-Based Analysis of the Surface Generation in Microendmilling—Part II: Experimental Validation and Analysis

[+] Author and Article Information
Xinyu Liu, Richard E. DeVor, Shiv G. Kapoor

Department of Mechanical and Industrial Engineering,  University of Illinois at Urbana-Champaign, Urbana, IL

J. Manuf. Sci. Eng 129(3), 461-469 (Nov 13, 2006) (9 pages) doi:10.1115/1.2716706 History: Received April 28, 2006; Revised November 13, 2006

The surface-generation models for the microendmilling process developed in Part I (Liu, DeVor, and Kapoor, 2007, J. Manuf. Sci. Eng., 129(3), pp. 453–460) are experimentally calibrated and validated. Partial immersion peripheral downmilling and full-immersion slotting tests are performed over a wide range of feed rates (0.2512μmflute) using two tools with different edge radii (3μm and 2μm) and runout levels (2μm and 3μm) for the investigation of sidewall and floor surface generation, respectively. The deterministic models are validated using large feed-rate tests with errors within 18% for both sidewall and floor surfaces. For low feed-rate tests, the stochastic portion of the surface roughness data are determined from the observed roughness data and the validated deterministic model. The stochastic models are then calibrated and validated using independent data sets. The combination of the deterministic and stochastic models predicts the total surface roughness within 15% for both the sidewall and floor surface over a range of feed rates. The models are then used to simulate micromachined surfaces under a variety of conditions to gain a deeper understanding of the effects of tool geometry (edge radius and edge serration), process conditions, tool tip runout, process kinematics and dynamics on the machined surface roughness.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 5

Variations of SPAf versus the distance from the slot center

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Figure 7

The predicted 3D surface topographies of the upmilled surface with four different parameter settings

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Figure 4

Stochastic surface roughness component of the floor surface

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Figure 6

The measured and simulated 2D surface roughness along the feed direction for different feed rates

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Figure 8

Predicted deterministic surface roughness Sa1 versus feed rates

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Figure 9

Predicted stochastic surface roughness Sa2 versus feed rates

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Figure 10

Predicted total surface roughness Sa versus feed rates

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Figure 11

Effect of dynamic vibrations on the 3D floor surface roughness

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Figure 12

Effect of edge radius on the 3D floor surface roughness

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Figure 13

Effect of clearance angle on the 3D floor surface roughness

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Figure 1

Total surface roughness Sa and deterministic surface roughness Sa1 for sidewall surface with varying feed rate

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Figure 2

Comparison of the stochastic model predictions of the stochastic surface roughness Sa2 for sidewall surface to those filtered from the deterministic model predictions

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Figure 3

Feed-rate trend of the total surface roughness Sa and deterministic surface roughness Sa1 for floor surface

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