2021
DOI: 10.3390/app11135759
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A Robot-Centered Path-Planning Algorithm for Multidirectional Additive Manufacturing for WAAM Processes and Pure Object Manipulation

Abstract: Previous algorithms for slicing, path planning or trajectory planning of additive manufacturing cannot be used consistently for multidirectional additive manufacturing with pure object manipulation in wire-arc additive manufacturing. This work presents a novel path planning approach that directly takes robot kinematics into account and thus ensures the reachability of all critical path poses. In an additional step, the planned path segments are smoothed so that joint velocity limits are respected. It is shown … Show more

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Cited by 23 publications
(13 citation statements)
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References 27 publications
(15 reference statements)
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“…For this application, pure OM is advantageous. For a pure OM holding the weld head including the periphery, the print component will perform large rotational movements [10].…”
Section: Robotic Planning: Pure Object Manipulationmentioning
confidence: 99%
See 3 more Smart Citations
“…For this application, pure OM is advantageous. For a pure OM holding the weld head including the periphery, the print component will perform large rotational movements [10].…”
Section: Robotic Planning: Pure Object Manipulationmentioning
confidence: 99%
“…The path planning algorithms related to this work were introduced by Schmitz et al [10]. For complex slices, the polygon is approximated, expanded, and then decomposed into multiple convex polygons for simplified planning.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…By laying successive layers of appropriate dimensions, it is possible to shape the final form of the product. Unfortunately, a few starts and stops of the arc passing from one layer to another cause transient phenomena that slow down the printing process, which can be eliminated by using CTPP-Continuous Three-dimensional Path Planning [14] or Robot-Centered Path-Planning [15]. Attention is also paid to the detection of defects using High Dynamic Range Images [16].…”
Section: Introductionmentioning
confidence: 99%