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

Abstract

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.

Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach

“…Consequently, surface roughness has a great influence on product quality, and the part functional properties such as lubricant retentivity, void volume, load bearing area, and frictional properties. Furthermore a good-quality machined surface significantly improves fatigue strength, corrosion resistance, and creep life (Stark and Moon, 1999). Surface roughness is consisting of a multitude of appar- * Corresponding author.…”

An experimental and theoretical analysis of surface generation in the ultra-precision grinding of hard and brittle materials

“…Surface quality is the crucial indicator to evaluate the grinding performance, which results from the interaction of the tool and machined material. A high-quality surface finish significantly improves the functional performance of components, such as fatigue strength, frictional properties and part life [8,9]. Surface generation, as an important issue in the grinding process, is directly related to the surface quality and functional performance of the workpiece.…”

Prediction of Surface Roughness Profiles for Milling Process with Fractal Parameters Based on LS-SVM

“…Consequently, surface roughness has a great influence on product quality, and the part functional properties such as lubricant retentivity, void volume, load bearing area ,and frictional properties. Furthermore a good quality machined surface significantly improve fatigue strength, corrosion， ， resistance and creep life [1].Surface roughness is consisting of a multitude of apparently random peaks and valleys. When two roughness surfaces are brought to be in contact, it is occurred in smaller area, which is called the real area of contact.…”