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ZHAO Junfu, DU Haiyuan, JIN Yongsheng, LI Jianjun. Research on real-time multi-task scheduling for distributed 3D printing services[J]. Manufacturing Technology & Machine Tool, 2024, (4): 188-195. doi: 10.19287/j.mtmt.1005-2402.2024.04.029
Citation: ZHAO Junfu, DU Haiyuan, JIN Yongsheng, LI Jianjun. Research on real-time multi-task scheduling for distributed 3D printing services[J]. Manufacturing Technology & Machine Tool, 2024, (4): 188-195. doi: 10.19287/j.mtmt.1005-2402.2024.04.029
shu

Research on real-time multi-task scheduling for distributed 3D printing services

doi: 10.19287/j.mtmt.1005-2402.2024.04.029
  • 1.

    Inner Mongolia University of Science & Technology, Baotou 014010, CHN

  • 2.

    Inner Mongolia Mengniu Dairy Group Company Limited, Hohhot 010000, CHN

  • Accepted Date: 2024-01-11
  • Rev Recd Date: 2023-11-10
    Abstract
  • Aiming at the problems of imbalanced workload distribution of 3D printing tasks (3DPTs) among distributed 3D printers (3DPs) in the process of sharing, collaborating, and producing globalized customized products in the Industrial Internet of Things (IIoT for short), as well as customized attributes and real-time performance of each submitted model, this essay propose a real-time green-aware multi-task scheduling architecture for personalized 3D printing in IIoT, and give a robust online allocation algorithm to solve the problem that each 3D printing task can accurately meet user-defined attributes as well as balance among distributed 3D printers. task scheduling architecture, giving a robust online allocation algorithm to solve the problem that each 3D printing task can accurately satisfy the user-defined attributes as well as balance the workload among distributed 3D printers, and developing a priority-based adaptive real-time multi-task scheduling (ARMPS) algorithm to schedule each 3D printing task in real-time to satisfy the real-time as well as dynamic requirements of 3D printing tasks. Simulation experiments are conducted under high load and performance evaluation tests show that the proposed algorithm is robust and the scheduling architecture is robust and scalable.

     

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