Aimed at inner quality controlling for complex surface parts, an ultrasonic testing system for complex surface parts has been developed using ultrasonic NDT(Non-destructive Testing)which has features of strong penetra...Aimed at inner quality controlling for complex surface parts, an ultrasonic testing system for complex surface parts has been developed using ultrasonic NDT(Non-destructive Testing)which has features of strong penetration, well direction, high sensitivity, low cost, and harmless to people and material. The technologies of the computer, NC (Numerical control), precision mechanism, signal analysis and processing were integrated in the testing system. The system includes a PC, system software, ultrasonic data acquisition card, stepper motor drive card and five-axis precision mechanical device, etc. The software was developed using WIN98-based VC++. According to CAD data of the parts and interpolation methods, the scanning programs can be programmed. The five-axis scanning system is driven by the CNC(computer numerical control) system to control the attitude of ultrasonic probes. The system’s automatic scanning for complex surface parts, real-time acquiring ultrasonic data and automatic identifying flaw signal have been realized. This system can be used not only for testing complex surface parts, but for testing random curve parts. With fast testing speed, high sensitivity, high testing precision and high reliability, the system has a wide adaptability.展开更多
Ambiguity function （AF） is proposed to represent ultrasonic signal to resolve the preprocessing problem of different center frequencies and different arriving times among ultrasonic signals for feature extraction, a...Ambiguity function （AF） is proposed to represent ultrasonic signal to resolve the preprocessing problem of different center frequencies and different arriving times among ultrasonic signals for feature extraction, as well as offer time-frequency features for signal classification. Moreover, Karhunen-Loeve （K-L） transform is considered to extract signal features from ambiguity plane, and then the features are presented to probabilistic neural network （PNN） for signal classification. Experimental results show that ambiguity function eliminates the difference of center frequency and arriving time existing in ultrasonic signals, and ambiguity plane features extracted by K-L transform describe the signal of different classes effectively in a reduced dimensional space. Classification result suggests that the ambiguity plane features obtain better performance than the features extracted by wavelet transform （WT）.展开更多
文摘Aimed at inner quality controlling for complex surface parts, an ultrasonic testing system for complex surface parts has been developed using ultrasonic NDT(Non-destructive Testing)which has features of strong penetration, well direction, high sensitivity, low cost, and harmless to people and material. The technologies of the computer, NC (Numerical control), precision mechanism, signal analysis and processing were integrated in the testing system. The system includes a PC, system software, ultrasonic data acquisition card, stepper motor drive card and five-axis precision mechanical device, etc. The software was developed using WIN98-based VC++. According to CAD data of the parts and interpolation methods, the scanning programs can be programmed. The five-axis scanning system is driven by the CNC(computer numerical control) system to control the attitude of ultrasonic probes. The system’s automatic scanning for complex surface parts, real-time acquiring ultrasonic data and automatic identifying flaw signal have been realized. This system can be used not only for testing complex surface parts, but for testing random curve parts. With fast testing speed, high sensitivity, high testing precision and high reliability, the system has a wide adaptability.
文摘Ambiguity function （AF） is proposed to represent ultrasonic signal to resolve the preprocessing problem of different center frequencies and different arriving times among ultrasonic signals for feature extraction, as well as offer time-frequency features for signal classification. Moreover, Karhunen-Loeve （K-L） transform is considered to extract signal features from ambiguity plane, and then the features are presented to probabilistic neural network （PNN） for signal classification. Experimental results show that ambiguity function eliminates the difference of center frequency and arriving time existing in ultrasonic signals, and ambiguity plane features extracted by K-L transform describe the signal of different classes effectively in a reduced dimensional space. Classification result suggests that the ambiguity plane features obtain better performance than the features extracted by wavelet transform （WT）.
