Modeling surface texture in the peripheral milling process using neural network, spline, and fractal methods with evidence of chaos

Modeling texture of milled surfaces using analytic methods requires explici t knowledge of a large number of variables some of which change during mach ining. These include dynamically changing tool runout, deflection, workpiec e material properties, displacement of the workpiece within its fixture and others. Due to the complexity of all factors combined, an alternative appr oach is presented utilizing the ability of neural networks and fractals to implicitly account for these combined conditions. In the initial model, pre dicted 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 su rface profiles on a small scale. The fractal character of surfaces as manif ested by the fractal dimension provides evidence of chaos in milling.