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Abstract Swarm manufacturing is an emerging manufacturing paradigm that employs a heterogeneous swarm of robots to accomplish complex hybrid manufacturing tasks. Cooperative 3D printing (C3DP), a specialized form of swarm manufacturing, enables multiple printers to collaboratively produce large-scale parts, addressing key tradeoffs in additive manufacturing, such as size, speed, quality, and cost. A fundamental challenge in C3DP is ensuring collision-free, time-optimal printing in a shared workspace. This is a complex problem that can be influenced by factors such as the number of printers, part geometry, printer positioning, mobility, and kinematics. In this article, we present SafeZone*, a collision-free and scalable C3DP framework that optimizes printing time by co-considering the geometry (area and shape) and topology (space-connectivity) of a shared workspace during layer partitioning. We first establish a conceptual framework to mathematically represent the topology of a layer through partition graphs. Then, we use a Voronoi tessellation within a constrained optimization framework to control the partition graph and minimize makespan. The Voronoi sites are associated with printer locations, allowing the framework to integrate physical constraints and facilitating solutions for systems with robotic manipulators. Physical testing in a four-printer scenario with robotic arms confirms that SafeZone* enables collision-free printing, resulting in a printing time reduction of 44.63% when compared to the single-printer scenario. Finally, numerical studies reveal trends in the optimal solutions concerning the chromatic number of their resulting partition graphs and the distribution of the printing areas among printers.
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Co-learning of extrusion deposition quality for supporting interconnected additive manufacturing systems
Additive manufacturing systems are being deployed on a cloud platform to provide networked manufacturing services. This article explores the value of interconnected printing systems that share process data on the cloud in improving quality control. We employed an example of quality learning for cloud printers by understanding how printing conditions impact printing errors. Traditionally, extensive experiments are necessary to collect data and estimate the relationship between printing conditions vs. quality. This research establishes a multi-printer co-learning methodology to obtain the relationship between the printing conditions and quality using limited data from each printer. Based on multiple interconnected extrusion-based printing systems, the methodology is demonstrated by learning the printing line variations and resultant infill defects induced by extruder kinematics. The method leverages the common covariance structures among printers for the co-learning of kinematics-quality models. This article further proposes a sampling-refined hybrid metaheuristic to reduce the search space for solutions. The results showed significant improvements in quality prediction by leveraging data from data-limited printers, an advantage over traditional transfer learning that transfers knowledge from a data-rich source to a data-limited target. The research establishes algorithms to support quality control for reconfigurable additive manufacturing systems on the cloud.
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- Award ID(s):
- 1901109
- PAR ID:
- 10345435
- Editor(s):
- Ding, Yu
- Date Published:
- Journal Name:
- IISE Transactions
- ISSN:
- 2472-5854
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Swarm manufacturing (SM) is an emerging manufacturing paradigm that employs a heterogeneous swarm of robots to accomplish complex hybrid manufacturing tasks. Cooperative 3D Printing (C3DP), a special form of swarm manufacturing, uses multiple printers to print large-scale parts cooperatively and aims to tackle key challenges in the additive manufacturing industry, such as trade-offs among size, speed, quality, and cost. A fundamental challenge in C3DP is how to achieve collision-free, time-efficient printing when multiple printers operate in a shared workspace. This is a complex problem since the solution may depend on a myriad of factors, such as the number of printers, part geometry, printer positioning, mobility, and kinematics, or whether the printing path pre-determined. In this paper, we present SafeZone, a collision-free and scalable C3DP framework that aims to minimize printing time by considering both the geometry and topology (space-connectivity) of the resulting workspace when segmenting the part layer. To achieve this, we use a guided Voronoi tessellation that can only produce degree-3 partitions, which we show to have optimal scheduling properties based on the chromatic number of the resulting partition graph. The sites of the Voronoi tessellation are constrained to only lie on the boundary of their convex hull, thus facilitating collision-free operation in C3DP systems with robotic arms. We demonstrate through physical testing in a 4-printer scenario with SCARA arms that SafeZone can produce collision-free prints, resulting in a printing time reduction of 44.63% when compared to the single-printer scenario. Finally, we show how the partition created by our methodology has a printing time reduction of 22.83% when compared to a naive choice which does not consider workspace topology.more » « less
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null (Ed.)Additive manufacturing processes, especially those based on fused filament fabrication mechanism, have a low productivity. One solution to this problem is to adopt a collaborative additive manufacturing system that employs multiple printers/extruders working simultaneously to improve productivity by reducing the process makespan. However, very limited research is available to address the major challenges in the co-scheduling of printing path scanning for different extruders. Existing studies lack: (i) a consideration of the impact of sub-path partitions and simultaneous printing of multiple layers on the multi-extruder printing makespan; and (ii) efficient algorithms to deal with the multiple decision-making involved. This article develops an improved method by first breaking down printing paths on different printing layers into sub-paths and assigning these generated sub-paths to different extruders. A mathematical model is formulated for the co-scheduling problem, and a hybrid algorithm with sequential solution procedures integrating an evolutionary algorithm and a heuristic is customized to multiple decision-making in the co-scheduling for collaborative printing. The performance was compared with the most recent research, and the results demonstrated further makespan reduction when sub-path partition or the simultaneous printing of multiple layers is considered. This article discusses the impacts of process setups on makespan reduction, providing a quantitative tool for guiding process development.more » « less
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Abstract One of the major challenges in 3D printing is its lack of scalability both in size and speed, which directly impacts its economic feasibility for large-scale industrial applications. Cooperative 3D printing (C3DP) is an emerging paradigm that aims to address these issues by employing multiple mobile printers that work in parallel. However, a crucial step in enabling C3DP is the development of a collision-free communication framework between the printers during the manufacturing process. Many C3DP systems found in the literature develop solutions for collision-free printing that are specific to the setup being used, thus not allowing the solution to be transferred to other similar systems. In this paper, we formulate a general framework that generates four distinct collision-free communication strategies to enable arm-arm coordination for C3DP using robotic manipulators. We considered collisions both between the arms with themselves and between the arms and the part being printed. The strategies are general in that they are agnostic to the number of printers, their kinematics, and their spatial configurations in the manufacturing environment. We conducted a study of the four strategies using a two-printer scenario and then physically validated them with four test cases of varying geometries. The results show that the strategies successfully produce printed parts while being collision-free. The makespan reduction using our strategies when compared to a single printer varied from 20% to 42% depending on the strategy used. Finally, we discuss the limitations of the framework, as well as future research directions.more » « less
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In this work, an additive manufacturing process for extruding fully compounded thermosetting elastomers based on fluorine-containing polymer compositions is reported. Additive manufacturing printers are designed with a dry ice container to precool filaments made from curable fluoroelastomer (FKM) and perfluoroelastomer (FFKM) compounds. A support tube guides the stiffened filament towards the printer nozzle. This support tube extends near the inlet to a printer nozzle. This approach allows low-modulus, uncured rubber filaments to be printed without buckling, a phenomenon common when 3D printing low-modulus elastomers via the fused deposition modeling (FDM) process. Modeling studies using thermal analyses data from a Dynamic Mechanical Analyzer (DMA) and a Differential Scanning Calorimeter (DSC) are used to calculate the Young’s modulus and buckling force, which helps us to select the appropriate applied pressure and the nozzle size for printing. Using this additive manufacturing (AM) method, the successful printing of FKM and FFKM compounds is demonstrated. This process can be used for the future manufacturing of seals or other parts from fluorine-containing polymers.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/24725854.2022.2080306
