Robotic conformal material extrusion 3D printing for appending structures on unstructured surfaces
Fabrication of structures in unstructured conditions is a promising area of bolstering the application spaces of additive manufacturing (AM). One emerging application is appending structures on existing ones that may have nonplanar surfaces in unconventional orientations. However, extrusion-based AM...
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Main Authors: | , , , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2024
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/178502 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Fabrication of structures in unstructured conditions is a promising area of bolstering the application spaces of additive manufacturing (AM). One emerging application is appending structures on existing ones that may have nonplanar surfaces in unconventional orientations. However, extrusion-based AM techniques are limited to printing on structured, planar environments with a fixed single-nozzle direction. Herein, the authors present a dexterous conformal material extrusion printing method using a six-axis robotic arm capable of constructing complex parts onto highly unstructured surfaces with rough topographies. The manufacturing method employs a custom algorithm that generates layers consisting of 3D spatial coordinates of print path as well as the extrusion nozzle oriented in the normal direction of the substrate, thereby enabling conformal motion of the extrusion nozzle to the unstructured surface. The capabilities of the surface-informed robotic conformal 3D printing method to fabricate structures on surfaces with a variety of topographies in unconventional orientations are demonstrated. Finally, via addition of deposited conductive paths, a high-strength, functional reinforcement capable of in situ deformation monitoring is appended. This work has the potential for reconstructing, repairing, and reinforcing existing structures in human-limited or inaccessible spaces. Integration of functional elements can also enable in situ sensing, monitoring, and self-diagnosis. |
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