Chatter stability in robotic milling

Cordes, Marcel; Hintze, Wolfgang; Altintaş, Yusuf

doi:10.1016/j.rcim.2018.07.004

Cited by 136 publications

(51 citation statements)

References 27 publications

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“…With the information on the captured process forces and the compliance model, the tool path can be controlled and the accuracy of an IR for machining application can therefore be increased. A structural dynamics of an articulated manipulator with a spindle and a tool was modelled by Cordes et al [65], of which predicted stability chart (predicted stability chart for an aluminium milling shown in Fig. 2) was experimentally validated.…”

Section: Machining Vibrationmentioning

confidence: 99%

“…Fig. 4 Predicted stability chart for an aluminium milling process considering four modal subsystems [65] In corresponding with robot stiffness, robot trajectory optimisation, related to tool path of workpiece, has been studied to optimise the robot configuration, feed speed and orientation, and cutting condition along with the trajectory.…”

Section: Trajectory Optimisationmentioning

confidence: 99%

See 1 more Smart Citation

Industrial robotic machining: a review

Wang

2019

Int J Adv Manuf Technol

244

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For the past three decades, robotic machining has attracted a large amount of research interest owning to the benefit of cost efficiency, high flexibility and multi-functionality of industrial robot. Covering articles published on the subjects of robotic machining in the past 30 years or so; this paper aims to provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works, and an understanding of the future directions in the field. The research works are organised into two operation categories, low material removal rate (MRR) and high MRR, according their machining properties, and the research topics are reviewed and highlighted separately. Then, a set of statistical analysis is carried out in terms of published years and countries. Towards an applicable robotic machining, the future trends and key research points are identified at the end of this paper.

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Section: Machining Vibrationmentioning

confidence: 99%

Section: Trajectory Optimisationmentioning

confidence: 99%

Industrial robotic machining: a review

Wang

2019

Int J Adv Manuf Technol

244

View full text Add to dashboard Cite

show abstract

“…A cutting force model for cylindrical helical end milling is incorporated into the equation of motion of the robot to obtain the force vector F E , without considering the regenerative effect and the effect of the dynamic response of the robot on the cutting force [40,41]. In our system, up milling is chosen due to the concern with a backlash of the system.…”

Section: Milling Force Modelmentioning

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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“…To obtain basis for vibration suppression, structure optimization and path planning, low order natural frequencies are particularly concerned among dynamic parameters. It is proposed that low order natural frequencies of robot are decided by the configurations, while the high order natural frequencies are mainly related to the machining system, which do not vary with robot configurations [25], this phenomenon is demonstrated in Figure 1. Thus, a method to predict natural frequency of 6R industrial robot efficiently and precisely is proposed in this paper.…”

Section: Introductionmentioning

confidence: 98%

Natural Frequency Prediction Method for 6R Machining Industrial Robot

2020

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The industrial robot machining performance is highly dependent on dynamic behavior of the robot, especially the natural frequency. This paper aims at introducing a method to predict the natural frequency of a 6R industrial robot at random configuration, for improving dynamic performance during robot machining. A prediction model of natural frequency which expresses the mathematical relation between natural frequency and configuration is constructed for a 6R robot. Joint angles are used as input variables to represent the configurations in the model. The quantity and range of variables are limited for efficiency and practicability. Then sample configurations are selected by central composite design method due to its capacity of disposing nonlinear effects, and natural frequency data is acquired through experimental modal test. The model, which is in form of regression equation, is fitted and optimized with sample data through partial least square (PLS) method. The proposed model is verified with random configurations and compared with the original model and a model fitted by least square method. Prediction results indicate that the model fitted and optimized by PLS method has the best prediction ability. The universality of the proposed method is validated through implementation onto a similar 6R robot.

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Chatter stability in robotic milling

Cited by 136 publications

References 27 publications

Industrial robotic machining: a review

Industrial robotic machining: a review

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

Natural Frequency Prediction Method for 6R Machining Industrial Robot

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