Modeling Surface Texture in the Peripheral Milling Process Using Neural Network, Spline, and Fractal Methods with Evidence of Chaos

[+] Author and Article Information
G. A. Stark, K. S. Moon

Department of Mechanical Engineering—Engineering Mechanics, Michigan Technological University, Houghton, MI

J. Manuf. Sci. Eng 121(2), 251-256 (May 01, 1999) (6 pages) doi:10.1115/1.2831213 History: Received December 01, 1996; Revised December 01, 1997; Online January 17, 2008


Modeling texture of milled surfaces using analytic methods requires explicit knowledge of a large number of variables some of which change during machining. These include dynamically changing tool runout, deflection, workpiece material properties, displacement of the workpiece within its fixture and others. Due to the complexity of all factors combined, an alternative approach is presented utilizing the ability of neural networks and fractals to implicitly account for these combined conditions. In the initial model, predicted surface points are first connected using splines to model 3D surface maps. Results are presented over varying several cutting parameters. Then, replacing splines, an improved fractal method is presented that determines fractal characteristics of milled surfaces to model more representative surface profiles on a small scale. The fractal character of surfaces as manifested by the fractal dimension provides evidence of chaos in milling.

Copyright © 1999 by The American Society of Mechanical Engineers
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