Improving machining accuracy with robot deformation compensation

Wang, Jianjun; Zhang, Hui; Fuhlbrigge, Thomas

doi:10.1109/iros.2009.5353988

Cited by 66 publications

(35 citation statements)

References 6 publications

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“…Traditionally, robot stiffness is modeled as six rotational stiffnesses around the z-axes of the joints [5], [11]. Due to lack of sufficient identification data, a linear description of joint stiffness, or compliance (inverse of the stiffness) is used.…”

Section: Stiffness Modeling and Identification Of Industrial Robotsmentioning

confidence: 99%

“…Different approaches have been present in literature to overcome these sources of errors. Besides hardware improvements, literature is focusing on online compensation based on additional sensing and actuation [5], [6], [7]. In addition to online compensation, the COMET project presents also approaches how model knowledge can be used in offline COMET steps towards accurate milling with robots: Model identification, offline path compenstation, online tracking and external actuation [8] programming [8] (see Fig.…”

Section: Introductionmentioning

confidence: 99%

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Automatic pose optimization for robotic processes

Schneider

Posada

Verl

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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In many robotic processes such as milling and drilling, there are multiple solutions for robot poses, as the rotation around the tool axis remains as a degree of freedom (DoF) in positioning. Yet until now, this DoF causes additional efforts in CAM programming as it requires manual intervention. Instead, this DoF can be used to optimize the robot pose according to different criteria of the robot such as stiffness or avoidance of backlash effects. This paper presents different criteria for optimization of the robot pose in machining and describes the optimization of robot stiffness based on a novel method for its identification, which is in detail described on this paper. Furthermore, the potential of the automatic resolution of the DoF is outlined enabling staff without robot knowledge to define reasonable robot paths

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Section: Stiffness Modeling and Identification Of Industrial Robotsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic pose optimization for robotic processes

Schneider

Posada

Verl

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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“…To simplify this problem, working pieces are assumed to be in a theoretical position, and all installation errors can be concentrated on the robot coordinate system [8,9]. The fundamental theory of base detection can be illustrated, first, by the actual position being checked through measuring the datum holes on the workpiece when the drilling robot system arrives at the designated location and, second, as the matrix between the theoretical robot coordinate system and the actual robot coordinate system being deduced to a generally correct position of the holes involved in programming.…”

Section: Introductionmentioning

confidence: 99%

Base Detection Research of Drilling Robot System by Using Visual Inspection

Wang

Bai

Zhang

et al. 2018

Journal of Robotics

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This paper expounds the principle and method of calibration and base detection by using the visual measurement system for detection and correction of installation error between workpiece and the robot drilling system. This includes the use of Cognex Insight 5403 high precision industrial camera, a light source, and the KEYENCE coaxial IL-300 laser displacement sensor. The threebase holes method and two-base holes method are proposed to analyze the transfer relation between the basic coordinate system of the actual hole drilling robot and the basic coordinate system of the theoretical hole drilling robot. The corresponding vision coordinates calibration and the base detection experiments are examined and the data indicate that the result of base detection is close to the correct value.

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“…Pan and Wang introduced an online deformation compensation approach based on a robot structure model for controlling the material removal rate. This approach controls the machining force by adjusting the robot feed speed in real time for improving part quality [19][20][21]. Conclusions from Pan and Wang determine that "The pattern of the process structure deformation is related to all of the following parameters: robot configuration, the location in the workspace, and the direction as well as the magnitude of the process force" [20], depicting the relevance to optimize robot configurations in relation to the robot stiffness.…”

Section: Introductionmentioning

confidence: 99%

Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning

et al. 2017

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Optimal and intuitive robotic machining is still a challenge. One of the main reasons for this is the lack of robot stiffness, which is also dependent on the robot positioning in the Cartesian space. To make up for this deficiency and with the aim of increasing robot machining accuracy, this contribution describes a solution approach for optimizing the stiffness over a desired milling path using the free degree of freedom of the machining process. The optimal motion is computed based on the semantic and mathematical interpretation of the manufacturing process modeled on its components: product, process and resource; and by configuring automatically a sample-based motion problem and the transition-based rapid-random tree algorithm for computing an optimal motion. The approach is simulated on a CAM software for a machining path revealing its functionality and outlining future potentials for the optimal motion generation for robotic machining processes.

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Improving machining accuracy with robot deformation compensation

Cited by 66 publications

References 6 publications

Automatic pose optimization for robotic processes

Automatic pose optimization for robotic processes

Base Detection Research of Drilling Robot System by Using Visual Inspection

Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning

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