叶片气膜孔加工缺陷的DR数字成像自动检测方法

气膜孔作为涡轮叶片中重要的冷却结构之一,对提高航空发动机冷却效率、降低燃烧室温度乃至提升发动机服役寿命等具有重要意义.然而,在实际加工过程中受外界因素干扰极易出现气膜孔未打通、打偏及损伤内壁等内部缺陷.传统检测手段采用单一透照角度的X射线胶片照片法对其内部缺陷进行识别,但存在检测效率低、测量误差大、缺陷漏检误判率高等弊端.为此,提出了一种基于X射线数字成像技术的气膜孔缺陷自动检测方法,通过研究DR数字图像与胶片图像的等效性、透照角度对缺陷识别的影响规律及检测工艺优化、基于深度学习神经网络缺陷自识别等关键技术等,实现对叶片气膜孔加工缺陷的高精度检测与智能识别.

Research on DR digital imaging automatic detection method of blade air film hole processing defects

Air film holes are one of the important cooling structures in turbine blades, it has great significance to improve the cooling efficiency of aircraft engines, reduce the temperature of the combustion chamber and even increase the service life of the engine. However, in the actual course of working, it is easy to have internal defects such as unopened air film holes, deflection, and damage to the inner wall due to external factors. Traditional detection methods mainly use artificial film X-ray method to detect internal defects, but there are problems such as low detection efficiency, high cost and high dependence on the experience and skills of the inspectors. This paper proposes an automatic detection method for air film hole defects based on X-ray digital imaging technology. By studying the equivalence of DR digital images and film images, the law of the effect of the transillumination angle on defect recognition and the optimization of the detection process, key technologies based on deep learning neural network defect self-recognition, high-precision detection and intelligent recognition of blade air film hole processing defects are realized.