机翼蒙皮无损检测生产线中扫描路径优化方法

Scanning Path Optimization Method in Wing Skin Nondestructive Testing Production Line

复合材料机翼蒙皮尺寸大、形状复杂、易回弹,无法采用机床等传统方式进行无损检测,机器人柔性化、智能化的特点,为无损检测生产线提供了全新的思路。针对此类问题,提出了大型机翼蒙皮连续曲面无损检测生产线及路径优化方法。利用双机器人搭载超声扫描设备,采用两次检测策略,第一次扫描对复合材料曲面进行重建,提高第二次透射式无损检测的精度。根据机翼的形貌,提出了一种平行于桁条行切的总体扫描策略,根据曲率利用最小二乘法将点集分组,之后应用混合遗传LM算法进行路径优化,即采用改进型遗传算法进行启发式全局优化,之后采用LM算法进行确定性局部优化,高效得到其最优扫描路径。在RoboDK中进行仿真,利用机器人搭载超声检测末端对蒙皮进行扫描。最后采用机器人搭载线激光扫描仪对优化路径进行精度验证。仿真及试验结果表明,相较于传统的检测方法,该方法满足无损检测约束条件,平均检测效率提高9.2%。

The composite wing skin has the characteristics of large size, complex shape and easy rebound. It can’t be used for nondestructive testing by traditional methods such as machine tools. But the robot has the characteristics of flexibility and intelligence, which provides a new idea for nondestructive testing. A scanning path optimization method of large wing skin continuous surface is proposed to solve this kind of problems. Dual robots equipped with ultrasonic scanning equipment, adopting the strategy of two-time inspection: the composite surface is reconstructed by the first scanning, so the accuracy of the second transmission nondestructive inspection is improved. According to the shape of the wing, a general scanning strategy parallel to the stringer is proposed. The points are grouped by the least squares method according to the curvature, the path is optimized by the hybrid genetic LM algorithm. The algorithm means the improved genetic algorithm is used for heuristic global optimization and the LM algorithm is used for deterministic local optimization, so that the optimal scanning path can be obtained efficiently. Then, the simulation is carried out in RoboDK, and the robot is equipped with ultrasonic detection end to scan the skin. Finally, the precision of the optimized path is verified by the laser scanner on the robot. Simulation and experiment results show that, compared with traditional detection methods, the average detection efficiency of this method is improved by 9.2%. It meets the constraints of ultrasonic detection.