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Research on Tool Wear Condiction Regniton Based on Acoustic Emission and Neral Network

Author: XieXiuZuo
Tutor: FuPan
School: Southwest Jiaotong University
Course: Measuring Technology and Instruments
Keywords: Tool wear Acoustic emission Wavelet Packet Analysis BP network Virtual instrument
CLC: TG71
Type: Master's thesis
Year: 2005
Downloads: 447
Quote: 9
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Abstract


The tool is one of the basic factors of production in the manufacturing process , it 's a direct impact on the quality of the processed objects , processing costs and production efficiency , and so , therefore , became a critical condition monitoring tool state detection technology link. The study showed that the tool condition monitoring to 75% reduction in downtime caused by human and technical factors , the effective processing time increased to 65 % from 10% of the monitoring system , the machine utilization increased to 50% . In this thesis, as the starting point of the acoustic emission signals , by build tool wear state of the experimental system and hands-on , get a great deal of research data . Randomness features of acoustic emission signals , the paper proposes to extract the signal characteristics of wavelet packet decomposition , wavelet analysis method is a very powerful tool to analyze the non-stationary random signal analysis results show that the energy extracted wavelet packet decomposition characteristics accurately reflect tool wear state . The neural network is the main method of pattern recognition . In this thesis, the nonlinear mapping function of the neural network , the establishment of the neural network , the mapping between the tool wear state acoustic emission signal eigenvectors , at the same time , also discussed the selection of the hidden layers of the network , the number of hidden layer neurons and network training . The papers through the establishment of the BP neural network to achieve the recognition of the state of tool wear overcome other identification methods to place undue reliance on the shortcomings of the experience and background knowledge . LabVIEW is a graphical programming language , is currently the most widely and fastest-growing , most powerful integrated development environment for graphical software . In this thesis, Matlab Script node build integrated acoustic emission signal processing based on LabVIEW and BP neural network recognition platform . The platform LabVIEW and Matlab tool wear state recognition simplistic , convenient and integration .

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CLC: > Industrial Technology > Metallurgy and Metal Craft > Cutting tools, abrasives,abrasive tools,fixtures,molds and hand tools > Tool
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