Investigation on Process-aware Based Intelligent Modeling of Bottom Layer Manufacturing Resources and Self-adaptive Collaborative Optimization Methodology

doi:10.3901/JME.2018.16.001

Abstract

Abstract: Current manufacturing systems have challenges in smart management and control, real-time synchronization, and collaborative optimization. To solve these challenges, a self-adaptive architecture of manufacturing resources is proposed in the Internet of Things and perceptive manufacturing environment. Under this architecture, three key technologies are thoroughly analysed and investigated based on emerging cyber-physical systems(CPS) and industrial internet of things(ⅡoT). Firstly, intelligent modelling of bottom layer manufacturing resources is conducted based on CPS and internet of manufacturing things(IoMT). Secondly, variable granularity self-adaptive collaborative control strategy and method for manufacturing systems is developed by using control theory. Thirdly, "event-status" multilevel supervision control mechanism is proposed for discrete event dynamic systems. Then, using system-level variable granularity self-adaptive collaboration strategy, the smart models of manufacturing resources with perception, interaction, and autonomous decision-making capabilities are developed to realize self-adaptive collaborative optimization during execution stage. The proposed strategy, method, and model provide the important theoretical basis and technical reference for next generation smart factory.

Key words: collaborative optimization, control theory, cyber-physical systems, industrial internet of things, manufacturing resources intelligence, smart manufacturing

CLC Number:

TH166

ZHANG Yingfeng, GUO Zhengang, QIAN Cheng, LI Rui. Investigation on Process-aware Based Intelligent Modeling of Bottom Layer Manufacturing Resources and Self-adaptive Collaborative Optimization Methodology[J]. Journal of Mechanical Engineering, 2018, 54(16): 1-10.

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