Pose optimization in robotic machining using static and dynamic stiffness models

Cvitanic, Toni; Nguyen, Vinh; Melkote, Shreyes N.

doi:10.1016/j.rcim.2020.101992

Cited by 65 publications

(25 citation statements)

References 27 publications

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“…According to the by Cvitanic et al [33], through the static stiffness model which was developed with decoupled partial pose method, the joint static stiffness values are given as:…”

Section: Placement Optimizationmentioning

confidence: 99%

Stiffness-Oriented Placement Optimization of Machining Robots for Large Component Flexible Manufacturing System

et al. 2022

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A large component flexible manufacturing system provides more application scenarios for industrial robots, and, in turn, these robots exhibit competitive advantages in machining applications. However, the structural characteristic of low stiffness is the main obstacle for the industrial robot. Aiming at obtaining sufficient stiffness in the whole machining process, this paper focuses on robot placement optimization in the flexible manufacturing of large components. The geometric center of the machined feature is selected as, firstly, the base point, and the center-reachable placement space of the robot base is obtained by establishing the kinematic model considering a variety of motion constraints. Then, according to the reachability of the machining feature contour, the global placement space meeting all machining boundaries is further extracted. The mapping relationship between joint force and posture is established, and the most suitable robot placement is selected based on the criterion of global stiffness optimization. A series of numerical and finite element simulations verify the correctness and effectiveness of the proposed optimization strategy. The developed stiffness-oriented placement planning algorithm can provide beneficial references for robotic machining applications.

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“…According to the by Cvitanic et al [33], through the static stiffness model which was developed with decoupled partial pose method, the joint static stiffness values are given as:…”

Section: Placement Optimizationmentioning

confidence: 99%

Stiffness-Oriented Placement Optimization of Machining Robots for Large Component Flexible Manufacturing System

et al. 2022

View full text Add to dashboard Cite

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“…Likewise, the number of investigations carried out in robotics is also increasing, allowing the tasks they can perform to be made more efficient [3] [4]. Among the tasks that a robot can carry out, one of the essential aspects that must be determined is how its displacement will be carried out [5], known as path planning.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Robotic Training in Virtual Environments using Deep Learning

Pinzón-Arenas¹,

Moreno²,

Rubiano³

2022

International Journal on Advanced Science, Engineering and Information Technology

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Reducing costs in the acquisition of industrial robots and their benefit in continuous production workdays have increased the number of investigations that expand the robot's action capabilities. This work proposes a trajectory planning system for a UR3 robot that is very usual in academic, research, and industrial applications. The system is presented based on a convolutional network training for regression tasks focused on learning the desired trajectory. A virtual environment has been developed to simulate different trajectories based on the interaction between UR3 robot and object detection and location through the convolutional network employed. This work exposes the network's training and the results of the transport of the object, where the robot can position itself on the desired tool (scissors and screwdriver), which is recognized by training a Faster network R-CNN and the re-localization of the tool in a conveyor band. For the trained trajectory's, a ResNet-50 model is proposed, and the overall performance achieved was 92.63%, with a mean square error of 24.7 mm in the trained trajectory's repetition. Also, the boxplot of each ax in the trajectory is exposed since they show in a more detailed way the deviation of each of the points in the whole validation set. The average collection time, from when the system takes the workspace capture to its initial positioning after leaving the tool on the belt, was 51.3 seconds, enough for real-time applications.

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“…Currently, industrial robots are increasingly being used in medicine [7], as well as in the processing of materials. Such types of mechanical processing as milling [8,9], polishing [10], as well as the use of robots in additive manufacturing, for example, in the technology of direct metal deposition of powder materials, are actively studied [11]. The use of direct metal deposition technology, in which controlled continuous multi-axis deposition of metal powder particles in the laser radiation field is carried out, provided by combining a gaspowder jet with a laser beam, is one of the progressive directions for the creation of aircraft engine parts [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Development of an Experimental Robotic Complex for Direct Metal Deposition and Testing of Deposition Modes for Heat-Resistant Powder Material

Oleynik

2022

Materials Research Proceedings

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Abstract. The paper considers the issue of optimizing the movement of an industrial robot used in additive manufacturing in the technology of direct metal deposition of parts. The developed mathematical model that takes into account the joint work of a six-axis robot manipulator and a two-axis positioner is described. The algorithm for calculating the motion based on the relative position of two adjacent points of the working tool trajectory relative to the rotary axis of the positioner with a given accuracy is described. The simulation of processing is carried out both when working only with the manipulator, and when working together with a two-axis positioner, and control programs with recalculated coordinates and rotation angles of the positioner are obtained.

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Pose optimization in robotic machining using static and dynamic stiffness models

Cited by 65 publications

References 27 publications

Stiffness-Oriented Placement Optimization of Machining Robots for Large Component Flexible Manufacturing System

Stiffness-Oriented Placement Optimization of Machining Robots for Large Component Flexible Manufacturing System

Path Planning for Robotic Training in Virtual Environments using Deep Learning

Development of an Experimental Robotic Complex for Direct Metal Deposition and Testing of Deposition Modes for Heat-Resistant Powder Material

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