The Real-Time Prediction of Surface Roughness Based on Genetic Wavelet Network

Jun Chi; Lian Qing Chen

doi:10.4028/www.scientific.net/AMR.102-104.610

Paper Titles

Study on the Effect of Ultrasonic Surface Rolling Processing Parameters on the Surface Roughness of Q345 Hydraulic Prop
p.591

Research on Hole Expanding with Spiral-Fluted Electrode in ECM
p.595

Tool Wear Image Segmentation Based on Markov Random Field Model
p.600

Layer Face Milling Cutter Parametric Modeling and Modal Analysis
p.605

The Real-Time Prediction of Surface Roughness Based on Genetic Wavelet Network
p.610

Study on the Cutting Deforming Force of Single Abrasive Grit for Ultrasonic Vibration Assisted Grinding along Normal Direction
p.615

Crossbeam Analysis and Structure Optimization of Special-Shaped Stone Combined Machining Center
p.620

Theoretical Analysis and Uniformity of Trajectories in Lapping Process
p.625

Research on Temperature Field about Cutting Area in Plunge Milling Ti Alloy Based on Finite Element
p.630

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The Real-Time Prediction of Surface Roughness Based on Genetic Wavelet Network

Abstract:

A methodology based on relax-type wavelet network was proposed for predicting surface roughness. After the influencing factors of roughness model were analyzed and the modified wavelet pack algorithm for signal filtering was discussed, the structure of artificial network for prediction was developed. The real-time forecast on line was achieved by the nonlinear mapping and learning mechanism in Elman algorithm based on the vibration acceleration and cutting parameters. The weights in network were optimized using genetic algorithm before back-propagation algorithm to reduce learning time.The validation of this methodology is carried out for turning aluminum and steel in the experiments and its prediction error is measured less than 3%.