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1. Improved co-scheduling of multi-layer printing path scanning for collaborative additive manufacturing

   https://doi.org/10.1080/24725854.2020.1807076  

   Jiang, Zhengqian; Wang, Hui; Sun, Yanshuo (January 2020, IISE Transactions)

   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. 

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2. Architecting the Cooperative 3D Printing System

   https://doi.org/10.1115/DETC2020-22711  

   Poudel, Laxmi; Marques, Lucas Galvan; Williams, Robert Austin; Hyden, Zachary; Guerra, Pablo; Fowler, Oliver Luke; Moquin, Stephen Joe; Sha, Zhenghui; Zhou, Wenchao (August 2020, 40th Computers and Information in Engineering Conference (CIE))

   null (Ed.)

   Abstract Cooperative 3D printing (C3DP) is a novel approach to additive manufacturing, where multiple mobile 3D printing robots work together cooperatively to print the desired part. At the core of C3DP lies the chunk-based printing strategy. This strategy splits the desired part into smaller chunks, and then the chunks are assigned and scheduled to be printed by individual printing robots. In our previous work, we presented various hardware and software components of C3DP, such as mobile 3D printers, chunk-based slicing, scheduling, and simulation. In this study, we present a fully integrated and functional C3DP platform with all necessary components, including chunker, slicer, scheduler, printing robots, build floor, and outline how they work in unison from a system-level perspective. To realize C3DP, new developments of both hardware and software are presented, including new chunking approaches, scalable scheduler for multiple robots, SCARA-based printing robots, a mobile platform for transporting printing robots, modular floor tiles, and a charging station for the mobile platform. Finally, we demonstrate the capability of the system using two case studies. In these demonstrations, a CAD model of a part is fed to the chunker, divided into smaller chunks, passed to the scheduler, and assigned and scheduled to be printed by the scheduler with a given number of robots. The slicer generates G-code for each of the chunks and combines G-code into one file for each robot. The simulator then uses the G-code generated by the slicer to generate animations for visualization purposes. 

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3. Design De-Identification of Thermal History for Collaborative Process-Defect Modeling of Directed Energy Deposition Processes

   https://doi.org/10.1115/1.4056488  

   Fullington, Durant; Bian, Linkan; Tian, Wenmeng (May 2023, Journal of Manufacturing Science and Engineering)

   Abstract There is an urgent need for developing collaborative process-defect modeling in metal-based additive manufacturing (AM). This mainly stems from the high volume of training data needed to develop reliable machine learning models for in-situ anomaly detection. The requirements for large data are especially challenging for small-to-medium manufacturers (SMMs), for whom collecting copious amounts of data is usually cost prohibitive. The objective of this research is to develop a secured data sharing mechanism for directed energy deposition (DED) based AM without disclosing product design information, facilitating secured data aggregation for collaborative modeling. However, one major obstacle is the privacy concerns that arise from data sharing, since AM process data contain confidential design information, such as the printing path. The proposed adaptive design de-identification for additive manufacturing (ADDAM) methodology integrates AM process knowledge into an adaptive de-identification procedure to mask the printing trajectory information in metal-based AM thermal history, which otherwise discloses substantial printing path information. This adaptive approach applies a flexible data privacy level to each thermal image based on its similarity with the other images, facilitating better data utility preservation while protecting data privacy. A real-world case study was used to validate the proposed method based on the fabrication of two cylindrical parts using a DED process. These results are expressed as a Pareto optimal solution, demonstrating significant improvements in privacy gain and minimal utility loss. The proposed method can facilitate privacy improvements of up to 30% with as little as 0% losses in dataset utility after de-identification. 

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4. A Generative Approach for Scheduling Multi-Robot Cooperative Three-Dimensional Printing

   https://doi.org/10.1115/1.4047261  

   Poudel, Laxmi; Zhou, Wenchao; Sha, Zhenghui (December 2020, Journal of Computing and Information Science in Engineering)

   null (Ed.)

   Abstract Cooperative 3D printing (C3DP) is a novel approach to additive manufacturing, where multiple printhead-carrying mobile robots work cooperatively to print the desired part. The core of C3DP is the chunk-based printing strategy in which the desired part is first split into smaller chunks and then the chunks are assigned to individual robots to print and bond. These robots will work simultaneously in a scheduled sequence to print the entire part. Although promising, C3DP lacks a generative approach that enables automatic chunking and scheduling. In this study, we aim to develop a generative approach that can automatically generate different print schedules for a chunked object by exploring a larger solution space that is often beyond the capability of human cognition. The generative approach contains (1) a random generator of diverse print schedules based on an adjacency matrix that represents a directed dependency tree structure of chunks; (2) a set of geometric constraints against which the randomly generated schedules will be checked for validation, and (3) a printing time evaluator for comparing the performance of all valid schedules. We demonstrate the efficacy of the generative approach using two case studies: a large simple rectangular bar and a miniature folding sport utility vehicle (SUV) with more complicated geometry. This study demonstrates that the generative approach can generate a large number of different print schedules for collision-free C3DP, which cannot be explored solely using human heuristics. This generative approach lays the foundation for building the optimization approach of C3DP scheduling. 

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5. Computational Design of Scheduling Strategies for Multi-Robot Cooperative 3D Printing

   https://doi.org/10.1115/DETC2019-97640  

   Poudel, Laxmi; Zhou, Wenchao; Sha, Zhenghui (August 2019, 39th Computers and Information in Engineering Conference)

   null (Ed.)

   Cooperative 3D printing (C3DP) is a novel approach to additive manufacturing, where multiple printhead-carrying mobile robots work together cooperatively to print a desired part. The core of C3DP is the chunk-based printing strategy in which the desired part is first split into smaller chunks, and then the chunks are assigned to individual printing robots. These robots will work on the chunks simultaneously and in a scheduled sequence until the entire part is complete. Though promising, C3DP lacks proper framework that enables automatic chunking and scheduling given the available number of robots. In this study, we develop a computational framework that can automatically generate print schedule for specified number of chunks. The framework contains 1) a random generator that creates random print schedule using adjacency matrix which represents directed dependency tree (DDT) structure of chunks; 2) a set of geometric constraints against which the randomly generated schedules will be checked for validation; and 3) a printing time evaluation metric for comparing the performance of all valid schedules. With the developed framework, we present a case study by printing a large rectangular plate which has dimensions beyond what traditional desktop printers can print. The study showcases that our computation framework can successfully generate a variety of scheduling strategies for collision-free C3DP without any human interventions. 

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