3117. Exploring self-organization and self-adaption for smart manufacturing complex networks
Invited abstract in session WC-50: Systems Thinking 5, stream Systems Thinking.
Wednesday, 12:30-14:00Room: Parkinson B11
Authors (first author is the speaker)
| 1. | Zhengang Guo
|
| Industrial Engineering, Northwestern Polytechnical University | |
| 2. | Yingfeng Zhang
|
| Northwestern Polytechnical University | |
| 3. | Sichao Liu
|
| KTH Royal Institute of Technology | |
| 4. | Xi Wang
|
| KTH Royal Institute of Technology | |
| 5. | Lihui Wang
|
| KTH Royal Institute of Technology |
Abstract
Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments. The Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber-physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. An industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time.
Keywords
- Manufacturing
- Graphs and Networks
- Optimal Control
Status: accepted
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