id |
cdrf2023_526 |
authors |
Eric Peterson, Bhavleen Kaur |
year |
2023 |
title |
Printing Compound-Curved Sandwich Structures with Robotic Multi-Bias Additive Manufacturing |
doi |
https://doi.org/https://doi.org/10.1007/978-981-99-8405-3_44
|
source |
Proceedings of the 2023 DigitalFUTURES The 5st International Conference on Computational Design and Robotic Fabrication (CDRF 2023) |
summary |
A research team at Florida International University Robotics and Digital Fabrication Lab has developed a novel method for 3d-printing curved open grid core sandwich structures using a thermoplastic extruder mounted on a robotic arm. This print-on-print additive manufacturing (AM) method relies on the 3d modeling software Rhinoceros and its parametric software plugin Grasshopper with Kuka-Parametric Robotic Control (Kuka-PRC) to convert NURBS surfaces into multi-bias additive manufacturing (MBAM) toolpaths. While several high-profile projects including the University of Stuttgart ICD/ITKE Research Pavilions 2014–15 and 2016–17, ETH-Digital Building Technologies project Levis Ergon Chair 2018, and 3D printed chair using Robotic Hybrid Manufacturing at Institute of Advanced Architecture of Catalonia (IAAC) 2019, have previously demonstrated the feasibility of 3d printing with either MBAM or sandwich structures, this method for printing Compound-Curved Sandwich Structures with Robotic MBAM combines these methods offering the possibility to significantly reduce the weight of spanning or cantilevered surfaces by incorporating the structural logic of open grid-core sandwiches with MBAM toolpath printing. Often built with fiber reinforced plastics (FRP), sandwich structures are a common solution for thin wall construction of compound curved surfaces that require a high strength-to-weight ratio with applications including aerospace, wind energy, marine, automotive, transportation infrastructure, architecture, furniture, and sports equipment manufacturing. Typical practices for producing sandwich structures are labor intensive, involving a multi-stage process including (1) the design and fabrication of a mould, (2) the application of a surface substrate such as FRP, (3) the manual application of a light-weight grid-core material, and (4) application of a second surface substrate to complete the sandwich. There are several shortcomings to this moulded manufacturing method that affect both the formal outcome and the manufacturing process: moulds are often costly and labor intensive to build, formal geometric freedom is limited by the minimum draft angles required for successful removal from the mould, and customization and refinement of product lines can be limited by the need for moulds. While the most common material for this construction method is FRP, our proof-of-concept experiments relied on low-cost thermoplastic using a specially configured pellet extruder. While the method proved feasible for small representative examples there remain significant challenges to the successful deployment of this manufacturing method at larger scales that can only be addressed with additional research. The digital workflow includes the following steps: (1) Create a 3D digital model of the base surface in Rhino, (2) Generate toolpaths for laminar printing in Grasshopper by converting surfaces into lists of oriented points, (3) Generate the structural grid-core using the same process, (4) Orient the robot to align in the direction of the substructure geometric planes, (5) Print the grid core using MBAM toolpaths, (6) Repeat step 1 and 2 for printing the outer surface with appropriate adjustments to the extruder orientation. During the design and printing process, we encountered several challenges including selecting geometry suitable for testing, extruder orientation, calibration of the hot end and extrusion/movement speeds, and deviation between the computer model and the physical object on the build platen. Physical models varied from their digital counterparts by several millimeters due to material deformation in the extrusion and cooling process. Real-time deviation verification studies will likely improve the workflow in future studies. |
series |
cdrf |
email |
eric.peterson@fiu.edu |
full text |
file.pdf (12,166,845 bytes) |
references |
Content-type: text/plain
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last changed |
2024/05/29 14:04 |
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