基于双路FNN网络的固体火箭发动机壳体内缝检测方法研究

Research on inner seam detection method of solid rocket motor shell based on dual FNN network

火箭发动机壳体内部螺纹连接处缝隙的检测精度是衡量其质量的重要指标,由于发动机壳体内表面形貌复杂,因此内缝质量仅靠人工检测不仅效率低而且可靠性差。提出一种基于FNN网络的内缝视觉检测方法,以灰度共生矩阵和PCA算法构造图像的特征参数,训练FNN网络,将火箭发动机壳体内缝的粗加工面与精加工面进行分类,分类识别率98.8%;然后,对两类情况做不同的图像处理,用Sobel算子找到缝隙边缘;最后,通过标定进行包括采集原始图像误差、直线拟合误差的系统误差修正,完成内缝宽度精确测量。实验表明,该方法稳定可靠,能够实现0.1~0.6 mm范围内±0.02 mm的识别精度。该方法实现了火箭发动机壳体内部螺纹连接处的高精度测量,为实现产品高效自动生产和质量检测提供了技术保障。

The detection accuracy of the gap in the internal threaded joint of a rocket engine is an important indicator of its quality. Due to the complex internal surface of the engine shell, the quality of the internal gap is not only low in efficiency but also in poor reliability by manual inspection. Proposing a visual inspection method for inward seams based on FNN network. The feature parameters of the image are constructed with gray-level co-occurrence matrix and PCA algorithm, and the FNN network is trained to classify and classify the rough and finished surfaces of the internal seams of rocket engine shells. The recognition rate is 98.8%. Then, different image processing is performed for the two types of situations, and the Sobel operator is used to find the edge of the gap. Finally, the system error of the algorithm(collecting the original image error, the straight line fitting error) is corrected through calibration, and the internal slit is completed Width is accurately measured. Experiments show that the method is stable and reliable, and can achieve a recognition accuracy of ±0.02 mm in the range of 0.1~0.6 mm. This method realizes the high-precision measurement of the threaded joints inside the rocket engine shell, and provides technical guarantee for the realization of high-efficiency automatic production and quality inspection of products.