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.

Copyright © 2007 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 1

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

Grahic Jump Location
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

Grahic Jump Location
Figure 3

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

Grahic Jump Location
Figure 5

Variations of SPAf versus the distance from the slot center

Grahic Jump Location
Figure 6

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

Grahic Jump Location
Figure 7

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

Grahic Jump Location
Figure 8

Predicted deterministic surface roughness Sa1 versus feed rates

Grahic Jump Location
Figure 9

Predicted stochastic surface roughness Sa2 versus feed rates

Grahic Jump Location
Figure 10

Predicted total surface roughness Sa versus feed rates

Grahic Jump Location
Figure 11

Effect of dynamic vibrations on the 3D floor surface roughness

Grahic Jump Location
Figure 13

Effect of clearance angle on the 3D floor surface roughness

Grahic Jump Location
Figure 12

Effect of edge radius on the 3D floor surface roughness

Grahic Jump Location
Figure 4

Stochastic surface roughness component of the floor surface




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In