Calibration and control of a redundant robotic workcell for milling tasks

Andrés, Javier; Gracia, Luis; Tornero, Josep

doi:10.1080/0951192x.2011.566284

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…Andres et al [73] utilized a redundancy resolution method based on minimization of a position-dependent scalar performance index that seeks to keep the robot pose away from ill-conditioned poses and away from joint limits. Experimental assessment of their redundancy resolution method was conducted on a KUKA manipulator for milling a relatively soft polymeric material.…”

Section: Offline Methodsmentioning

confidence: 99%

Robots in machining

et al. 2019

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Robotic machining centers offer diverse advantages: large operation reach with large reorientation capability, and a low cost, to name a few. Many challenges have slowed down the adoption or sometimes inhibited the use of robots for machining tasks. This paper deals with the current usage and status of robots in machining, as well as the necessary modelling and identification for enabling optimization, process planning and process control. Recent research addressing deburring, milling, incremental forming, polishing or thin wall machining is presented. We discuss various processes in which robots need to deal with significant process forces while fulfilling their machining task.

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Section: Offline Methodsmentioning

confidence: 99%

Robots in machining

et al. 2019

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“…With the development of modern metrology techniques such as laser tracking [6,7] and photogrammetry-based measurement [8,9], the robotic positioning accuracy can be improved by kinematic parameter calibration [10][11][12] and positioning error compensation [7,8,[13][14][15][16]. For example, Andres et al [12] developed a calibration method to estimate the external joint configuration parameters using a laser displacement sensor. Morris et al [16] developed a kinematic model for a robot arm through a coordinate measuring machine (CMM), and experimental measurements at some robot poses were taken using the CMM.…”

Section: Introductionmentioning

confidence: 99%

Workpiece Pose Optimization for Milling with Flexible-Joint Robots to Improve Quasi-Static Performance

Qin

Xiong

2019

Applied Sciences

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Although industrial robots are widely used in production automation, their applications in machining have been limited because of the structural vibrations induced by periodic cutting forces. Since the dynamic characteristics of an industrial robot depends on its configuration, the responses of the robot structure to the cutting forces are affected by how the workpiece is placed within the workspace of the robot. This paper presents a method for workpiece pose optimization for a robotic milling system to improve the quasi-static performance during machining. Since the milling forces are time-varying due to the characteristics of the multi-tooth and discontinuity of milling, these forces can excite vibrations inside the robot structure. To address this issue, a structural dynamics model is established for industrial robots, considering their joint flexibility, and a milling force formulation is incorporated into the robot dynamics model to investigate the forced vibrations of the flexible joints. Then, the quasi-static performance of the robotic machining system is evaluated by the vibration-induced offset of the cutter tool that is mounted on the end-effector. Finally, an optimization approach is given for the workpiece pose to minimize the cutter tool offset under the periodic milling force. A numerical simulation demonstrates that the optimal workpiece pose can significantly reduce the overall tool offset during machining and can lower the variation of the tool offset along the milling path.

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“…It is outstanding that the cutter's tracking data are directly related with the desired finish conditions of the workpiece. Thus, these data are mandatory and independent from the machine tool that will manufacture the workpiece (putting particular calibration efforts on one side [1]). However, this information has to be postprocessed (i.e., adapted) from the CAM system to the production system that is going to be used.…”

Section: Introductionmentioning

confidence: 99%

Implementation and testing of a CAM postprocessor for an industrial redundant workcell with evaluation of several fuzzified Redundancy Resolution Schemes

Andrés

Gracia

Tornero

2012

Robotics and Computer-Integrated Manufacturing

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Calibration and control of a redundant robotic workcell for milling tasks

Cited by 10 publications

References 25 publications

Robots in machining

Robots in machining

Workpiece Pose Optimization for Milling with Flexible-Joint Robots to Improve Quasi-Static Performance

Implementation and testing of a CAM postprocessor for an industrial redundant workcell with evaluation of several fuzzified Redundancy Resolution Schemes

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