CAJ | 학술논문

复杂零部件作为制造领域中常用零件，其特征提取质量对其自动化精密加工有着重要影响，特别是其边缘轮廓以及表面纹理。而当前的特征提取算法难以应用于复杂零部件，其检测效果不理想；且检测效率不佳，是一种非并行模式，增加了其检测成本。对此，为了提高复杂零部件特征的检测精度与效率，文章提出了基于实时更新神经滤波及其光学实现的复杂零部件特征检测算法。基于Hopfield神经网络，构造自适应滤波掩码，并嵌入2 D卷积运算，设计了实时更新神经滤波技术及其学习算法，利用学习算法更新滤波系数与偏差值；并建立了该技术的光学联合转换结构，完成神经滤波的光学实现，提取复杂零部件特征，并提高其检测效率。仿真实验结果显示：与当前边缘检测机制相比，文章算法良好的检测精度，曲面连接处角点丰富，轮廓连续完整，拓扑凸显后，部件表面形貌清晰可见；且该算法的检测效率更高。
복잡령부건작위제조영역중상용령건，기특정제취질량대기자동화정밀가공유착중요영향，특별시기변연륜곽이급표면문리。이당전적특정제취산법난이응용우복잡령부건，기검측효과불이상；차검측효솔불가，시일충비병행모식，증가료기검측성본。대차，위료제고복잡령부건특정적검측정도여효솔，문장제출료기우실시경신신경려파급기광학실현적복잡령부건특정검측산법。기우Hopfield신경망락，구조자괄응려파엄마，병감입2 D권적운산，설계료실시경신신경려파기술급기학습산법，이용학습산법경신려파계수여편차치；병건립료해기술적광학연합전환결구，완성신경려파적광학실현，제취복잡령부건특정，병제고기검측효솔。방진실험결과현시：여당전변연검측궤제상비，문장산법량호적검측정도，곡면련접처각점봉부，륜곽련속완정，탁복철현후，부건표면형모청석가견；차해산법적검측효솔경고。
Thread is an indispensable coupling member in the field of machinery, and its detection accuracy was determined by the tooth edge type extraction. In order to improve the quality and efficiency of thread tooth edge extraction, the thread image feature extraction algorithm based on real time updated neural filte-ring and its optical implement was proposed. The real time updated neural filtering technology was designed to extract thread tooth edge by basing on Hopfield neural networks and embedding adjustable filter mask and 2D convolution operation;and also the phase-only joint transform correlation was built to implement the dy-namic neural filtering for improving the detection speed. The simulation results showed that:compared with current thread edge extraction mechanism, the detection quality and efficiency of this algorithm was higher.