System Design and Monitoring Method of Robot Grinding for Friction Stir Weld Seam

Li, Mingyang; Du, Zhijiang; Ma, Xiaoxing; Gao, Kui; Dong, Wei; Yan, Di; Gao, Yongzhuo

doi:10.3390/app10082903

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…The expected value selection method can overcome the random error phenomenon that individuals with high fitness may be eliminated and individuals with low fitness may be selected [14][15][16] and improve the real-time performance of genetic algorithm. The specific steps are as follows:…”

Section: Expected Value Selection Methodmentioning

confidence: 99%

Improved Genetic Algorithm for Intelligent Grinding Trajectory of Industrial Robot Sensor

2022

Journal of Sensors

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In order to study the improved genetic algorithm for intelligent grinding trajectory of industrial robot, a trajectory planning method with optimal energy consumption is proposed. Firstly, the trajectory of the robot is regarded as a series of type value points in space, and each adjacent type value point is connected by a quintic B -spline curve to obtain the trajectory function of the robot. Then, the kinetic energy is taken as the target energy consumption function, and the kinematic and dynamic constraints of each joint are considered at the same time. Finally, the genetic algorithm is improved to optimize the objective energy consumption function. The improved genetic algorithm improves the operation efficiency, local search ability, and real-time performance of the algorithm. Front and rear times during handling were 15.034 s and 17.456 s for 2.422 s. The optimal secondary trajectory planning algorithm is called in the single workstation of the welding robot and the robot welding the bucket with spatial straight line and spatial curve, respectively. Through the time difference, the time used in the spatial linear welding is 5.462 s, and the spatial curve welding time is 12.981 s. The proposed algorithm can be applied to the production of various industrial sensor robots such as welding robot, cutting robot, and spraying robot and improves the working efficiency of robot operation. In addition, the genetic method of multichromosome structure also has a certain reference for solving the general GSTP problem.

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Section: Expected Value Selection Methodmentioning

confidence: 99%

Improved Genetic Algorithm for Intelligent Grinding Trajectory of Industrial Robot Sensor

2022

Journal of Sensors

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“…In industrial applications, especially in manufacturing systems, the main research includes the extraction of point cloud feature information and point cloud registration. In previous research, the authors proposed a point cloud registration method based on hierarchical equal area isoLatitude Pixelation of a sphere (Gao et al , 2019), a point cloud extraction method of weld seam based on graph-cut (Li et al , 2020), and an analysis method of workpiece machining allowance based on point cloud (Gao et al , 2019). In the research object of this paper, we mainly focus on features such as workpiece edges and corners.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, there are many commercial tools, including radial and axial floating spindles. Li et al (2020) proposed a detailed model for artificial muscle that enables passive flexibility. However, in the application of this paper, it is difficult for these tools to guarantee the angle of the chamfer.…”

Section: Related Workmentioning

confidence: 99%

A robot chamfering system for special-shaped and thin-walled workpieces

et al. 2021

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Purpose This paper aims to propose a robotic automation system for processing special-shaped thin-walled workpieces, which includes a measurement part and a processing part. Design/methodology/approach In the measurement part, to efficiently and accurately realize the three-dimensional camera hand-eye calibration based on a large amount of measurement data, this paper improves the traditional probabilistic method. To solve the problem of time-consuming in the extraction of point cloud features, this paper proposes a point cloud feature extraction method based on seed points. In the processing part, the authors design a new type of chamfering tool. During the process, the robot adopts admittance control to perform compensation according to the feedback of four sensors mounted on the tool. Findings Experiments show that the proposed system can make the tool smoothly fit the chamfered edge during processing and the machined chamfer meets the processing requirements of 0.5 × 0.5 to 0.9 × 0.9 mm2. Practical implications The proposed design and approach can be applied on many types of special-shaped thin-walled parts. This will give a new solution for the automation integration problem in aerospace manufacturing. Originality/value A novel robotic automation system for processing special-shaped thin-walled workpieces is proposed and a new type of chamfering tool is designed. Furthermore, a more accurate probabilistic hand-eye calibration method and a more efficient point cloud extraction method are proposed, which are suitable for this system when comparing with the traditional methods.

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“…It is also necessary to use a tool that absorbs vibrations due to the contact force [ 11 ]. Another example can be found in [ 12 ], where researchers develop a specific end effector for grinding applications. In this study, an abrasive belt and a force-controlled grinding tool mounted in the end effector are used to improve finishing in welding seams.…”

Section: Introductionmentioning

confidence: 99%

Behavioural Study of the Force Control Loop Used in a Collaborative Robot for Sanding Materials

et al. 2020

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The rise of collaborative robots urges the consideration of them for different industrial tasks such as sanding. In this context, the purpose of this article is to demonstrate the feasibility of using collaborative robots in processing operations, such as orbital sanding. For the demonstration, the tools and working conditions have been adjusted to the capacity of the robot. Materials with different characteristics have been selected, such as aluminium, steel, brass, wood, and plastic. An inner/outer control loop strategy has been used, complementing the robot’s motion control with an outer force control loop. After carrying out an explanatory design of experiments, it was observed that it is possible to perform the operation in all materials, without destabilising the control, with a mean force error of 0.32%. Compared with industrial robots, collaborative ones can perform the same sanding task with similar results. An important outcome is that unlike what might be thought, an increase in the applied force does not guarantee a better finish. In fact, an increase in the feed rate does not produce significant variation in the finish—less than 0.02 µm; therefore, the process is in a “saturation state” and it is possible to increase the feed rate to increase productivity.

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System Design and Monitoring Method of Robot Grinding for Friction Stir Weld Seam

Cited by 12 publications

References 28 publications

Improved Genetic Algorithm for Intelligent Grinding Trajectory of Industrial Robot Sensor

Improved Genetic Algorithm for Intelligent Grinding Trajectory of Industrial Robot Sensor

A robot chamfering system for special-shaped and thin-walled workpieces

Behavioural Study of the Force Control Loop Used in a Collaborative Robot for Sanding Materials

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