Research And Realization Of The Mechanical Workpiece Defect Detection By Using Acoustic Signal

In recent years, the production of mechanical workpiece is increasing. Improving the quality of the production is becoming an issue of concern in many manufacturers. But the technology of defect diagnosis about mechanical workpiece started late and developed slowly, based on the introduction and digestion of foreign technology. The traditional detection methods are cumbersome, complex, and rely on artificial vision or hearing to discriminate. Therefore, design a concise and reliable mechanical workpiece defect diagnosis system has great practical significance.Firstly, this paper analyzes the defect cause of the mechanical workpiece and the acoustic emission phenomenon causes. Using the traditional spectral analysis to process the signal has the shortage of localized analysis. To overcome the shortage of this method, this paper introduces the wavelet transform method, especially the wavelet decomposition theory of ideal filter. Based on wavelet theory, an overall program of mechanical workpiece quality defect diagnosis system is designed. In this program, this system is divided into two different parts. One is the realization of the signal analysis software and another is the realization of the acoustic emission hardware circuit design.In order to enhance the precision of acoustic emission signal, this paper designs the acoustic emission sampling circuit with 24 sampling precision and 100 KHz sampling frequency rate and achieves the function of data transmission via USB interface. Based on the spectral analysis, this paper researches the decomposition process of wavelet package and its algorithm. Then, build the wavelet package energy spectrum characteristics and write the mechanical workpiece defect diagnosis user interface software accordingly with Lab/Windows CVI.This paper set up the mechanical workpiece defect diagnosis experiment platform. Using this platform, we analysis the noise of signal acquisition circuit. This paper compares the defective and zero defective spectrum. And then, comparing metal mechanical workpiece and powder metallurgy components by using wavelet package energy spectrum, we find that it is more effective for the powder metallurgy components than metal mechanical workpiece. In conclusion, this meet the practical application requirement.