Robotic Machining from Programming to Process Control

Pan, Zengxi; Zhang, Hui

doi:10.5772/9322

Cited by 2 publications

(1 citation statement)

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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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