Junshan Hu

Bio: Junshan Hu is an academic researcher from Nanjing University of Aeronautics and Astronautics. The author has contributed to research in topics: Rivet & Composite number. The author has an hindex of 2, co-authored 4 publications receiving 5 citations.

Stiffness properties analysis and enhancement in robotic drilling application

Abstract: Low stiffness and special stiffness properties limit the application of industrial robots in sophisticated manufacturing. The subject of the paper is to investigate the influence of robot stiffness properties on machining quality in drilling application. It is found that unidirectional thrust force could induce three-directional deformation of robot which will directly lead to hole defects during drilling procedure. Firstly, starting from the special characteristics of the robot, the key role of the preload pressing force is pointed out. Equivalent stiffness model under pre-load pressing force is established and stiffness promotion coefficient is defined to evaluate the effects of pressing force quantitatively. The matching criterion of robot drilling posture and thrust force is proposed, and the optimized value of pressing force can be predicted under the condition of stable machining. Limitation on hole diameter and roundness of robot drilling is evaluated too. By applying pre-load pressing force, the stiffness of robot drilling plane is markedly improved, and the drilling stability and hole diameter accuracy are also enhanced. Finally, the proposed method is verified by drilling experiments.

Effect of hole perpendicularity error and squeeze force on the mechanical behaviors of riveted joints.

Abstract: In the automatic drilling and riveting process, the perpendicular error of the hole is inevitable, which has a great influence on the assembly quality. In the current research, the shear and pull-out behaviors of riveted joints under different perpendicularity errors and squeeze forces were investigated and compared by the quasi-static tests. The fracture of the failed samples was characterized by a scanning electron microscope and the formation process of fracture was discussed. The failure mechanisms of riveted joints were analyzed in detail to guide engineering applications. The test results demonstrated that the shear load and pull-out load of riveted joints increased slightly with the increase of the tilt angle from 0° to 4°. The perpendicularity error did not affect the shear and pull-out failure modes of the riveted joints. However, the squeeze force had a significant effect on the failure modes of the pull-out samples. Fracture analysis showed that the failure of all shear samples occurred at the rivet shaft. Besides, when the squeeze force increased from 15 kN to 23 kN, the failure modes of the pull-out samples changed from the sheet to the rivet itself.

Posture optimization in robotic machining based on comprehensive deformation index considering spindle weight and cutting force

Abstract: Industrial robot has been considered to be the most promising choice to replace traditional CNC machine tool due to its low cost, high flexibility, versatility and large workspace. However, insufficient stiffness of the robot often leads to deformations and vibrations during the machining process. To improve the machining performance of robot, a posture optimization method through controlling the functional redundancy of robot is proposed. First, considering the spindle weight-induced deformation and cutting force-induced deformation simultaneously, a comprehensive deformation index is proposed to evaluate the stiffness performance of robot posture along the robot milling trajectory. Then, ideal stiffness interval at each cutter location point is calculated by minimizing the proposed deformation index in consideration of the constraints of joint and joint velocity. Next, the kinematics performance index is taken as the optimization target, and the postures of the entire milling trajectory are optimized collaboratively within the ideal stiffness intervals. In order to approximate the global optimal solution, the genetic algorithm is used to solve the optimal robot posture. Finally, a series of simulations and experiments are carried out to verify the proposed index and robot trajectory optimization method, and the results show that the machining accuracy can be efficiently improved by the proposed method.

Posture optimization in robotic machining based on comprehensive deformation index considering spindle weight and cutting force

Abstract: Industrial robot has been considered to be the most promising choice to replace traditional CNC machine tool due to its low cost, high flexibility, versatility and large workspace. However, insufficient stiffness of the robot often leads to deformations and vibrations during the machining process. To improve the machining performance of robot, a posture optimization method through controlling the functional redundancy of robot is proposed. First, considering the spindle weight-induced deformation and cutting force-induced deformation simultaneously, a comprehensive deformation index is proposed to evaluate the stiffness performance of robot posture along the robot milling trajectory. Then, ideal stiffness interval at each cutter location point is calculated by minimizing the proposed deformation index in consideration of the constraints of joint and joint velocity. Next, the kinematics performance index is taken as the optimization target, and the postures of the entire milling trajectory are optimized collaboratively within the ideal stiffness intervals. In order to approximate the global optimal solution, the genetic algorithm is used to solve the optimal robot posture. Finally, a series of simulations and experiments are carried out to verify the proposed index and robot trajectory optimization method, and the results show that the machining accuracy can be efficiently improved by the proposed method.

Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems

Abstract: The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide dynamic range. In this paper and for the first time, we propose a novel neuromorphic vision based controller for robotic machining applications to enable faster and more reliable operation, and present a complete robotic system capable of performing drilling tasks with sub-millimeter accuracy. Our proposed system localizes the target workpiece in 3D using two perception stages that we developed specifically for the asynchronous output of neuromorphic cameras. The first stage performs multi-view reconstruction for an initial estimate of the workpiece’s pose, and the second stage refines this estimate for a local region of the workpiece using circular hole detection. The robot then precisely positions the drilling end-effector and drills the target holes on the workpiece using a combined position-based and image-based visual servoing approach. The proposed solution is validated experimentally for drilling nutplate holes on workpieces placed arbitrarily in an unstructured environment with uncontrolled lighting. Experimental results prove the effectiveness of our solution with maximum positional errors of less than 0.2 mm, and demonstrate that the use of neuromorphic vision overcomes the lighting and speed limitations of conventional cameras. The findings of this paper identify neuromorphic vision as a promising technology that can expedite and robustify robotic manufacturing processes in line with the requirements of the fourth industrial revolution. • We present a neuromorphic visual controller approach for precise robotic machining. • We devise an event-based 3D reconstruction method for the localization of workpieces. • We develop an event-based method for the detection and tracking of circular objects. • We preform experiments that validate the proposed method in a robotic drilling setup. • The presented system can perform drilling tasks with sub-millimeter precision. • Results demonstrate the performance and robustness advantages of neuromorphic vision.