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Research Papers

# Virtual Five-Axis Flank Milling of Jet Engine Impellers—Part I: Mechanics of Five-Axis Flank Milling

[+] Author and Article Information
W. B. Ferry

Manufacturing Automation Laboratory, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canadaferry@interchange.ubc.ca

Y. Altintas

Manufacturing Automation Laboratory, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canadaaltintas@interchange.ubc.ca

J. Manuf. Sci. Eng 130(1), 011005 (Jan 30, 2008) (11 pages) doi:10.1115/1.2815761 History: Received November 29, 2006; Revised September 07, 2007; Published January 30, 2008

## Abstract

This work is the first of a two part paper on cutting force prediction and feed optimization for the five-axis flank milling of jet engine impellers. In Part I, a mathematical model for predicting cutting forces is presented for five-axis machining with tapered, helical, ball-end mills with variable pitch and serrated flutes. The cutter is divided axially into a number of differential elements, each with its own feed-coordinate system due to five-axis motion. At each element, the total velocity due to translation and angular motion is split into horizontal and vertical feed components, which are used to calculate total chip thickness along the cutting edge. The cutting forces for each element are calculated by transforming friction angle, shear stress, and shear angle from an orthogonal cutting database to the oblique cutting plane. The distributed cutting load is digitally summed to obtain the total forces acting on the cutter and blade. The model can be used for general five-axis flank milling processes, and supports a variety of cutting tools. Predicted cutting forces are shown to be in reasonable agreement with those collected during a roughing operation on a prototype integrally bladed rotor.

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## Figures

Figure 1

Example of the five-axis milling of a typical jet-engine impeller (source: CGTech VERICUT -TM)

Figure 2

A five-axis sculptured surface machining tool path segment

Figure 3

Illustration of cutter motion in five-axis flank milling. Cutter is translating along vector {vLi} (left), while rotating around {ki} (right). At each element, the total feed varies due to the combination of linear and angular motion.

Figure 4

(a) Chip thickness distributions due to horizontal feed (feed along the {XT} direction), (b) vertical feed (feed parallel to the tool axis, {ZT}), and (c) combined horizontal and vertical feeds

Figure 5

(a) Feed variation along the cutting edge from combined translational and rotational motion. (b) Total feed vector varies at each element. (c) Feed-coordinate system at each element is shifted by an angle, θs(z), relative to the feed-coordinate system at the tool tip.

Figure 6

(a) Profile of a serrated cutter. The radii of the flutes are different at each cross section. (b) Approximate path of each flute. (c) and (d) Illustrations showing how Eq. 22 searches back over previous feed-per-tooth values and flute lengths to find the minimum chip thickness.

Figure 7

CWE maps from the IBR roughing tool path. Triangular shapes on boundaries of the maps are due to the resolution of the engagement calculation algorithm.

Figure 8

Closeup of the engagement map for tool path Segment 67. Each block is defined by four parameters: zb, ab, ϕst, and ϕex.

Figure 9

Workpiece and roughing tool path used to compare predicted and simulated cutting forces. The workpiece is a titanium alloy blank for an IBR. Illustration was generated with the help of CGTech’s VERICUT (TM).

Figure 10

Comparisons of raw and filtered measured cutting forces and torques. (a)–(d) Raw and filtered data versus time for the entire tool path. (e) and (f) Fourier spectra of raw and filtered X and Z forces. (g) and (h) Closeups of X and Z forces.

Figure 11

(a)–(d) Predicted versus filtered experimental data for the entire tool path. (e)–(h) Closeups of predicted and measured X forces at various parts of the tool path.

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