Functional simulator of 3-axis parallel kinematic milling machine

2008 ◽  
Vol 42 (7-8) ◽  
pp. 813-821 ◽  
Author(s):  
Milos Glavonjic ◽  
Dragan Milutinovic ◽  
Sasa Zivanovic
Keyword(s):  
Milling Machine ◽  
Axis Parallel ◽  
Parallel Kinematic

Desktop 3-axis parallel kinematic milling machine

2009 ◽  
Vol 46 (1-4) ◽  
pp. 51-60 ◽  
Author(s):  
Milos Glavonjic ◽  
Dragan Milutinovic ◽  
Sasa Zivanovic ◽  
Zoran Dimic ◽  
Vladimir Kvrgic
Keyword(s):  
Milling Machine ◽  
Axis Parallel ◽  
Parallel Kinematic

Configuring and analysis of a class of generalized reconfigurable 2-axis parallel kinematic machine

2019 ◽  
Vol 33 (7) ◽  
pp. 3407-3421 ◽  
Author(s):  
Goran Vasilic ◽  
Sasa Zivanovic ◽  
Branko Kokotovic ◽  
Zoran Dimic
Keyword(s):  
Parallel Kinematic Machine ◽  
Axis Parallel ◽  
Parallel Kinematic

Kinematics Analysis of the Exechon Tripod

2010 ◽  
Author(s):  
Matteo Zoppi ◽  
Dimiter Zlatanov ◽  
Rezia Molfino
Keyword(s):  
Machine Tools ◽  
Inverse Kinematics ◽  
Three Dimensional ◽  
Kinematics Analysis ◽  
Axis Parallel ◽  
Spherical Wrist ◽  
In Series ◽  
Successful Design ◽  
Parallel Kinematic ◽  
Fixture System

The Exechon 5-Axis Parallel Kinematic Machine (PKM) is a successful design created in Sweden and adopted by many producers of machine tools around the world. A new version of the manipulator is being developed as a component of a mobile self-reconfigurable fixture system within an inter-European project. The basic Exechon architecture consists of a 3-degree-of-freedom (dof) parallel mechanism (PM) connected in series with a two- or three-dof spherical wrist. The PM has two UPR (4-dof) legs, constrained to move in a common rotating plane, and an SPR (5-dof) leg. The paper presents the kinematic analysis of both the PM and the hybrid parallel-serial architecture. We describe the complex three-dimensional motion pattern of the PM platform, derive the kinematic equations and provide explicit solutions for the inverse kinematics.

scholarly journals A five-axis parallel kinematic mirror unit for soft X-ray beamlines at MAX IV

2020 ◽  
Vol 27 (2) ◽  
pp. 262-271
Author(s):  
Marcus Agåker ◽  
Frieder Mueller ◽  
Brian Norsk Jensen ◽  
Karl Åhnberg ◽  
Peter Sjöblom ◽  
...  
Keyword(s):  
Mechanical Stability ◽  
Grazing Incidence ◽  
Fourth Generation ◽  
X Ray ◽  
Axis Parallel ◽  
Order Of Magnitude ◽  
Grazing Incidence Optics ◽  
Parallel Kinematic ◽  
Five Axis ◽  
Better Than

With the introduction of the multi-bend achromats in the new fourth-generation storage rings the emittance has decreased by an order of magnitude resulting in increased brightness. However, the higher brightness comes with smaller beam sizes and narrower radiation cones. As a consequence, the requirements on mechanical stability regarding the beamline components increases. Here an innovative five-axis parallel kinematic mirror unit for use with soft X-ray beamlines using off-axis grazing-incidence optics is presented. Using simulations and measurements from the HIPPIE beamline at the MAX IV Laboratory it is shown that it has no Eigen frequencies below 90 Hz. Its positioning accuracy is better than 25 nm linearly and 17–35 µrad angularly depending on the mirror chamber dimensions.

Dynamic Design for a Novel Parallel Kinematic Machine with Passive Linkage

2007 ◽  
Vol 364-366 ◽  
pp. 1037-1042
Author(s):  
Ying Hu ◽  
Bing Li ◽  
Dai Zhong Su ◽  
Hong Hu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Parallel Mechanism ◽  
Parallel Kinematic Machine ◽  
Element Analysis ◽  
Dynamic Design ◽  
Modeling Accuracy ◽  
Axis Parallel ◽  
Parallel Kinematic ◽  
Artificial Neural

Based on the proposed 4PUS-1RPU parallel mechanism a 5-axis Parallel Kinematic Machine (PKM) scheme has been developed and the dynamic characteristics of the PKM have been investigated in detail. To avoid the intensive computation caused by finite element analysis in the research, two typical metamodeling techniques of Response Surface Methodology (RSM) and its artificial neural network enhanced technique were employed as the robust design approaches. Comparing the results obtained from the direct RSM the modeling accuracy by the artificial neural network enhanced RSM can be improved.

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