Neural Network Prediction of Segment Wear in Stone Sawing

Xi Peng Xu; Y.F. Zhang

doi:10.4028/www.scientific.net/MSF.471-472.485

Paper Titles

Wear Mechanism of Diamond Mills in Stone Cutting
p.469

Research on Ultra-Precision Machining Technology for KTP
p.473

Two Approaches for Greenization of Fixture Design and Corresponding Technology Innovation
p.477

Development of Endless Diamond Wire Saw and Sawing Experiments
p.481

Neural Network Prediction of Segment Wear in Stone Sawing
p.485

A Study on Computer Simulation of Plastic Lens Molding Process
p.490

Design of High Precision Power Supply for Low-Frequency Vibration Cutting
p.494

Wrinkling Limit Based on FEM Virtual Experiment during NC Bending Process of Thin-Walled Tube
p.498

Forming Defects Analysis of Auto-Panel Stamped Part with Experimental Strain Approach
p.503

HomeMaterials Science ForumMaterials Science Forum Vols. 471-472Neural Network Prediction of Segment Wear in Stone...

Neural Network Prediction of Segment Wear in Stone Sawing

Abstract:

An investigation was carried out to predict the wear performance of diamond sawblade segments by using a Levenberg-Marquardt backpropagation (BP) neural network. The wear of the diamond segments were measured in circular sawing of natural gray granite in order to train the network and examine its validation. Since the depth of cut and workpiece speed are two main variables in the sawing of a specific granite material with a fixed diamond sawblade, a 2-5-1 structure of BP network was found to be capable of predicting the wear performance. In spite of the limited experimental data, the average value of relative errors between the simulated and measured results was found to be around 10%. Since experimentally measuring of segment wear is a time-consuming job, the trained network was also used to predict the wear performance under a very wide range of operating parameters, which can provide a useful guideline for the optimization of stone sawing.