机械工程学报 >

2018 , Vol. 54 >Issue 16: 1 - 10

DOI: https://doi.org/10.3901/JME.2018.16.001

特邀专栏：制造物联网与智能制造服务技术

基于过程感知的底层制造资源智能化建模及其自适应协同优化方法研究

  • 张映锋 ,
  • 郭振刚 ,
  • 钱成 ,
  • 李锐
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  • 西北工业大学现代设计与集成制造技术教育部重点实验室 西安 710072
郭振刚,男,1992年出生,博士研究生。主要研究方向为智能制造系统、生产物流协同等。E-mail:guozg@mail.nwpu.edu.cn;钱成,男,1993年出生,博士研究生。主要研究方向为智能制造系统。E-mail:qch.mail@qq.com;李锐,男,1991年出生,硕士研究生。主要研究方向为智能制造系统及协同控制。E-mail:heimuyao@163.com

收稿日期: 2017-07-11

  修回日期: 2017-12-28

  网络出版日期: 2018-08-20

基金资助

国家自然科学基金（51675441）和中央高校基本科研业务费专项（3102017jc04001）资助项目。

收起

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

  • ZHANG Yingfeng ,
  • GUO Zhengang ,
  • QIAN Cheng ,
  • LI Rui
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  • Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi'an 710072

Received date: 2017-07-11

  Revised date: 2017-12-28

  Online published: 2018-08-20

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摘要

在分析现有制造系统智能化管控、实时联动与协同优化方面所面临挑战的基础上，结合最新CPS和工业物联技术，提出了一种“物物互联，感知制造”环境下的底层制造资源智能化建模及其自适应协同优化体系。通过对基于CPS与制造物联的底层加工资源智能化建模、基于控制论的制造系统变粒度自适应协同控制策略与方法、离散事件动态系统“事件-状态”多级监管控制机制等关键技术的深入分析和研究，建立一种具有感知交互和自决策能力的智能加工设备和搬运小车模型，进而在制造系统级变粒度自适应协同控制策略下实现制造执行过程中各类底层智能制造资源的自适应协同优化，所提策略、方法和模型为下一代“智慧/智能工厂”的落地应用奠定了重要的理论基础和技术借鉴。

关键词： 智能制造; 制造资源智能化; 协同优化; 控制论; 工业物联; 信息物理系统

本文引用格式

张映锋 , 郭振刚 , 钱成 , 李锐 . 基于过程感知的底层制造资源智能化建模及其自适应协同优化方法研究[J]. 机械工程学报, 2018 , 54(16) : 1 -10 . DOI: 10.3901/JME.2018.16.001

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

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