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Special Section: Micromanufacturing

Intelligent Tool-Path Segmentation for Improved Stability and Reduced Machining Time in Micromilling

[+] Author and Article Information
J. Rhett Mayor

Precision Manufacturing Research Consortium, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 813 Ferst Drive NW, Atlanta, GA 30332rhett.mayor@me.gatech.edu

Angela A. Sodemann

Precision Manufacturing Research Consortium, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 813 Ferst Drive NW, Atlanta, GA 30332

J. Manuf. Sci. Eng 130(3), 031121 (Jun 25, 2008) (13 pages) doi:10.1115/1.2931492 History: Received August 07, 2007; Revised March 18, 2008; Published June 25, 2008

This paper presents the method of variable-feedrate intelligent segmentation as an enhanced approach to feedrate optimization in micromilling that overcomes detrimental scale effects in the process and leads to improved stability and decreased machining time. Due to the high tool-size to feature-size ratio present in micromilling, the maximum allowable feedrate is limited by the sampling rate of the real-time trajectory generation and motion control system. The variable-feedrate intelligent segmentation method is proposed to compensate for the feedrate limitation by intelligent selection of the interpolation technique applied to segments along the tool path in order to reduce the trajectory generation computation time and enable increased sampling frequency. The increased sampling frequency allows higher maximum feedrates providing for increased productivity and improved process stability. The performance of the novel intelligent segmentation approach was benchmarked against recent non-rational B-splines (NURBS) feedrate optimization techniques. Results from the numerical evaluation of the intelligent segmentation technique have demonstrated significant reductions in machining time, with a maximum reduction of over 50% recorded. Furthermore, the results from the study demonstrate the advantages of the intelligent segmentation method in enhancing process stability and maintaining, or marginally decreasing, process error. The variable feedrate intelligent segmentation method developed in this study provides, therefore, an enhanced methodology for path planning in high-speed, high-precision micromilling operations.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 1

Two primary sources of error, interpolation error and chord error, relative to desired tool CLs

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Figure 2

Trends of chord error, interpolation error, and total error with varying trajectory generation time, showing the minimum error points [A,A′,A″] as feedrate increases and feature size decreases

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Figure 3

Flowchart for the CB segmentation algorithm

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Figure 4

Flowchart for the SB segmentation algorithm

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Figure 5

Numerical evaluations of the proposed intelligent segmentation approach were performed on two case studies: (a) an externally machined fan shape and (b) an internal machined airfoil die cavity

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Figure 6

Segments identified by (a) CB segmentation and (b) SB segmentation for the fan shape feature

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Figure 7

Maximum and minimum feedrate limits for the fan shape feature, after (a) CB segmentation and (b) SB segmentation.

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Figure 8

Segments identified by (a) CB segmentation and (b) SB segmentation for the airfoil die

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Figure 9

Maximum and minimum feedrate limits for the airfoil die shape, after (a) CB segmentation and (b) SB segmentation

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Figure 10

Final results of the intelligent segmentation feedrate optimization method on the (a) fan shape and the (b) airfoil die cavity

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Figure 11

Number of segments identified by each interpolation approach for (a) the fan feature shape and (b) the airfoil die shape

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Figure 12

Minimum length of segments identified by each interpolation approach for (a) the fan feature shape and (b) the airfoil die shape

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Figure 13

Percent time benefit over VF NURBS method achieved by each interpolation approach by mean tool-size to feature-size ratio

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