Real-Time Imaging Detection Technology Of Carbon Fiber Composite

Carbon fiber, a new novel material, is of a series of excellent characteristics, such as low density, high strength, high modulus and low expansion coefficient. So that it is widely applied in many fields, including Aerospace, Power and Electronics, Sports Materials, Civil and Automotive. Unlike the novel material, it is not straightforward to discovery and predicts the breakage of Carbon Fiber, which often brings about major security incidents. So it is very important to analyze and detect the quality of Carbon Fiber Composite.After going deep into the research on the modern non-destructive testing technology, real-time imaging detection technology of carbon fiber composite based on X-Ray LDA(Linear Diode Arrays) is put forward, and the system of the Real-Time Imaging Detection of Carbon Fiber Based On X-Ray is realized.The high-precision CNC technology of PLC utilized in the system, which is used to control the orientation and speed of the motor, and accomplish the acquisition of the image data automatically. For the X-Ray image defects in large random noise, low contrast and dark grey, the method of locally adaptive wavelet domain demonizing to X-Ray image processing is applied to overcome the problem that the Gaussian white noise is effectively suppressed and the edge detail is preserved well. For the narrow grey range of the X-Ray image, the improved algorithm for multi-scale image contrast amplification makes better, and it is good to detect and recognize the flaws of the X-Ray furthermore. For the uneven and scattering characteristic of X-Ray Imaging, an algorithm by computing the minimum difference in the grey ratio for image mosaic is proposed, which is of high accuracy and good integration ,and seamless splicing for X-Ray images comes true finally. In conclusion, the system of the Real-Time Imaging Detection of Carbon Fiber Based on X-Ray fully meeting all the requirements of customers and manufacturers, and achieved good practical results.