Robotic machining from programming to process control

Pan, Zengxi; Zhang, Hui

doi:10.1109/wcica.2008.4594434

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…In this way, the robotic polishing system can regulate the polishing contact pressure by cooperating with an active end-effector. In this kind of robotic compliance control, the compliance-end-effector is usually mounted at a robot arm’s end [ 41 ]. However, for the polishing process under non-laboratory conditions, the structure of this end-effector is highly complex and challenging to maintain.…”

Section: Introductionmentioning

confidence: 99%

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing

Li,

Cheung,

Lam

et al. 2024

Sensors

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Compliance control strategies have been utilised for the ultraprecision polishing process for many years. Most researchers execute active compliance control strategies by employing impedance control law on a robot development platform. However, these methods are limited by the load capacity, positioning accuracy, and repeatability of polishing mechanisms. Moreover, a sophisticated actuator mounted at the end of the end-effector of robots is difficult to maintain in the polishing scenario. In contrast, a hybrid mechanism for polishing that possesses the advantages of serial and parallel mechanisms can mitigate the above problems, especially when an active compliance control strategy is employed. In this research, a high-frequency-impedance robust force control strategy is proposed. It outputs a position adjustment value directly according to a contact pressure adjustment value. An open architecture control system with customised software is developed to respond to external interrupts during the polishing procedure, implementing the active compliance control strategy on a hybrid mechanism. Through this method, the hybrid mechanism can adapt to the external environment with a given contact pressure automatically instead of relying on estimating the environment stiffness. Experimental results show that the proposed strategy adapts the unknown freeform surface without overshooting and improves the surface quality. The average surface roughness value decreases from 0.057 um to 0.027 um.

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Section: Introductionmentioning

confidence: 99%

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing

Li,

Cheung,

Lam

et al. 2024

Sensors

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“…The sources of the stiffness of a typical robot manipulator are the compliance of its joints, actuators and other transmission elements, geometric and material properties of the links, base, and the active stiffness provided by its position control system [4]. For the purpose of this study, we assume that: 1) the compliance in actuators and transmission elements is the dominant source of the stiffness, and it can be represented by a tortional spring for each joint [5]; 2) the active compliance in actuators due to a robot position control system provided by the original equipment manufacturer does not vary with time though an integral controller can increase the active compliance, depending on the positioning error; 3) the links are infinitely stiff.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Simulation of the Six Axis Machining Robot for Trajectory Planning in CATIA-LMS

Luo

Gibaru

Olabi

2012

AMM

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Nowadays six axis machining robots are widely used in many fields of industry. Compared to machine tools, industrial robots offer a cheaper yet more flexible alternative to the machine-tools in the cleaning and pre-machining applications of aluminum castings. But the low stiffness has limited the application of industrial robots to the machining tasks with very low precision requirement. This paper presents a practical approach to improve the robot-machining accuracy by developing an off line simulation tool. Firstly we will complete the dynamic simulation of the 6-axis stiff model in CATIA-LMS for trajectory planning. Secondly we will set flexible joints and balancing system for the industry machining robot in LMS. Finally we will make some compare with the position trajectories generated by flexible joint and stiff joint, and then adjust the parameters under the references of the simulation result before the industry machining.

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Human-Robot Collaboration and Sensor-Based Robots in Industrial Applications and Construction

Salmi¹,

Ahola²,

Heikkilä³

et al. 2018

Springer Series in Adaptive Environments

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Robotic machining from programming to process control

Cited by 8 publications

References 23 publications

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing

Dynamic Simulation of the Six Axis Machining Robot for Trajectory Planning in CATIA-LMS

Human-Robot Collaboration and Sensor-Based Robots in Industrial Applications and Construction

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