Support-free volume printing by multi-axis motion

Dai, Chengkai; Wang, Charlie C. L.; Wu, Chenming; Lefèbvre, Sylvain; Fang, Guoxin; Liu, Yong-Jin

doi:10.1145/3197517.3201342

Cited by 170 publications

(109 citation statements)

References 36 publications

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“…We also compare the results generated by our method to the graph-search based method with a denser sampling -i.e., the nodes in a ladder are generated by every degree for the value of θ in Eq.(4). This is actually the method used in [1]. Detail computational statistics of our trajectory planning algorithm in the robot-assisted 3D printing application can be found in Table II.…”

Section: B Results Of Jerk-optimized Trajectoriesmentioning

confidence: 99%

“…Many robot-assisted manufacturing tasks impose constraints on the robot's motion that exhibit kinematic redundancy, where more DOFs are available than the needed DOFs to realize the task. An example is to accumulate materials in 3D printing by tracing a given path with an axi-symmetric filament extruder [1], [12]. In robotic motion planning, there are two types of redundancy, intrinsic and functional ones.…”

Section: A Trajectory Planning For Task-oriented Constrainsmentioning

confidence: 99%

“…The efficient training is also very important as the shape of a workpiece under fabrication (also considered as obstacles) is changed from time to time. By using the routine developed in [1] for additive manufacturing, the collision-indication function needs to be updated when the fabrication process moves from one working surface layer to the next one. Similar concept of working surface layers can also be found in the subtractive manufacturing [36].…”

Section: A Approximate Representation Of Collision Functionmentioning

confidence: 99%

“…The trajectory of motion is often optimized for a robotic manipulator with kinematic redundancy -e.g., a six degree-of-freedom (DOF) robotic arm is employed to realize a trajectory with 5-DOF constraints (see Fig. 1 for an example in additive manufacturing [1]). Other than additive manufacturing, example processes include robotic machining [2], automated tape laying for composite fabrication [3], etc.…”

Section: Introductionmentioning

confidence: 99%

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Planning Jerk-Optimized Trajectory With Discrete Time Constraints for Redundant Robots

Dai

Lefèbvre

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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We present a method for effectively planning the motion trajectory of robots in manufacturing tasks, the toolpaths of which are usually complex and have a large number of discrete-time constraints as waypoints. Kinematic redundancy also exists in these robotic systems. The jerk of motion is optimized in our trajectory planning method at the meanwhile of fabrication process to improve the quality of fabrication. Our method is based on a sampling strategy and consists of two major parts. After determining an initial path by graph-search, a greedy algorithm is adopted to optimize a path by locally applying adaptive filers in the regions with large jerks. The filtering result is obtained by numerical optimization. In order to achieve efficient computation, an adaptive sampling method is developed for learning a collision-indication function that is represented as a support-vector machine. Applications in robotassisted 3D printing are given in this paper to demonstrate the functionality of our approach.Note to Practitioner Abstract-In robot-assisted manufacturing applications, robotic arms are employed to realize the motion of workpieces (or machining tools) specified as a sequence of waypoints with the positions of tool tip and the tool orientations constrained. The required degree-of-freedoms (DOFs) are often less than the robotic hardware system (e.g., a robotic arm has 6-DOFs). Specifically, rotations of the workpiece around the axis of a tool can be arbitrary (see Fig. 1 for an example). By using this redundancy -i.e., there are many possible poses of a robotic arm to realize a given waypoint, the trajectory of robots can be optimized to consider the performance of motion in velocity, acceleration and jerk in the joint space. In addition, when fabricating complex models each tool-path can have a large amount of waypoints. It is crucial for a motion planning algorithm to compute a smooth and collision-free trajectory of robot to improve fabrication quality. The time taken by the planning algorithm should not significantly lengthen the total manufacturing time; ideally it would remain hidden as computing motions for a layer can be done while the previous layer is printing. The method presented in this paper provides an efficient framework to tackle this problem. The framework has been well tested on our robot-assisted additive manufacturing system to demonstrate its effectiveness and can be generally applied to other robot-assisted manufacturing systems. Fig. 1. An example tool-path for robot-assisted 3D printing system [1], rotation around the red axis can be freely changed because of kinematic redundancy.

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Section: B Results Of Jerk-optimized Trajectoriesmentioning

confidence: 99%

Section: A Trajectory Planning For Task-oriented Constrainsmentioning

confidence: 99%

Section: A Approximate Representation Of Collision Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Planning Jerk-Optimized Trajectory With Discrete Time Constraints for Redundant Robots

Dai

Lefèbvre

et al. 2020

IEEE Trans. Automat. Sci. Eng.

Self Cite

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“…Vanek et al [VGB14a] describe a geometry‐based approach that minimizes the support material by finding the orientation requiring the least amount of support area and minimize the number of points needed to support overhangs. Most recently, Dai et al [DWW*18] propose a robotic printing system equipped with a multi‐axis motion designed in order to reduce the need for supporting structures.…”

Section: Previous Workmentioning

confidence: 99%

Object Partitioning for Support‐Free 3D‐Printing

Karasik

Fattal

Werman

2019

Computer Graphics Forum

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Fused deposition modeling based 3D‐printing is becoming increasingly popular due to it's low‐cost and simple operation and maintenance. While it produces rugged prints made from a wide range of materials, it suffers from an inherent printing limitation where it cannot produce overhanging surfaces of non‐trivial size. This limitation can be handled by constructing temporary support‐structures, however this solution involves additional material costs, longer print time, and often a fair amount of labor in removing it. In this paper we present a new method for partitioning general solid objects into a small number of parts that can be printed with no support. The partitioning is computed by applying a sequence of cutting‐planes that split the object recursively. Unlike existing algorithms, the planes are not chosen at random, rather they are derived from shape analysis routines that identify and resolve various commonly‐found geometric configurations. In addition, we guide this search by a revised set of conditions that both ensure the objects' printability as well as realistically model the printing capabilities of the printer at hand. Evaluation of the new method demonstrates its ability to efficiently obtain support‐free partitionings typically containing fewer parts compared to existing methods that rely on support‐structures.

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Conformal Geometry and Multimaterial Additive Manufacturing through Freeform Transformation of Building Layers

et al. 2021

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3D printing, formally known as additive manufacturing, creates complex geometries via layer‐by‐layer addition of materials. While 3D printing has been historically perceived as the static addition of build layers, 3D printing is now considered as a dynamic assembly process. In this context, here a new 3D printing process is reported that executes full degree‐of‐freedom (DOF) transformation (translating, rotating, and scaling) of each individual building layer while utilizing continuous fabrication techniques. Transforming individual building layers within the sequential layered manufacturing process enables dynamic transformation of the 3D printed parts on‐the‐fly, eliminating the time‐consuming redesign steps. Preserving the locality of the transformation to each layer further enables the discrete conformal transformation, allowing objects such as vascular scaffolds to be optimally fabricated to properly fit within specific patient anatomy obtained from the magnetic resonance imaging (MRI) measurements. Finally, exploiting the freedom to control the orientation of each individual building layer, multimaterials, multiaxis 3D printing capability are further established for integrating functional modules made of dissimilar materials in 3D printed devices. This final capability is demonstrated through 3D printing a soft pneumatic gripper via heterogenous integration of rigid base and soft actuating limbs.

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Support-free volume printing by multi-axis motion

Cited by 170 publications

References 36 publications

Planning Jerk-Optimized Trajectory With Discrete Time Constraints for Redundant Robots

Planning Jerk-Optimized Trajectory With Discrete Time Constraints for Redundant Robots

Object Partitioning for Support‐Free 3D‐Printing

Conformal Geometry and Multimaterial Additive Manufacturing through Freeform Transformation of Building Layers

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