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Engineering    2017, Vol. 3 Issue (5) : 608-615     https://doi.org/10.1016/J.ENG.2017.05.016
Research |
智能制造控制——多尺度研究领域的挑战
Han-Xiong Li(),Haitao Si()
Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China
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摘要 
《中国制造2025》计划要求从顾客到产品等全部环节的全面自动化。这将为生产制造系统的各个环节带来巨大挑战。在未来的加工制造领域中,全部的设备和系统应当具有对控制性和适应性的感知能力和基础智能化处理的能力。在研究中,经过关于多尺度动力学在现代加工制造系统中应用的讨论后,一个五层的功能结构被用于不确定的加工制造过程。多尺度力学包括:多时间尺度、多时空尺度以及多尺度的动力学标准。随着快速与慢速的时间尺度对设计的更多要求,不同的尺度所对应的不同控制行为也将呈现区分化。低速时间尺度下的操作需要更多的定量化手段,与此同时,高速时间尺度下的监管也需要更多高质量的手段。智能生产系统应当拥有灵活应变的能力、较好的适应能力及足够的智能化程度。这些能力需要我们通过控制性手段进行区分化处理并应用在不同方面,如智能感知、优化设计、智能学习等。最后,将一个典型的喷射点胶系统模型用于多尺度建模和控制。
关键词 系统建模过程控制人工智能加工制造喷射点胶    
Abstract

The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include: multi-time scale, space-time scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control.

Keywords System modeling      Process control      Artificial intelligence      Manufacturing      Jet dispensing     
基金资助: 
在线预览日期:    发布日期: 2017-11-08
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Han-Xiong Li
Haitao Si
引用本文:   
Han-Xiong Li,Haitao Si. Control for Intelligent Manufacturing: A Multiscale Challenge[J]. Engineering, 2017, 3(5): 608-615.
网址:  
http://engineering.org.cn/EN/10.1016/J.ENG.2017.05.016     OR     http://engineering.org.cn/EN/Y2017/V3/I5/608
Property Machine level Plant-wide level
Characteristics Local
(product oriented)
Global
(business oriented )
Dynamics Fast Slow
Complexity Small scale
(linear dominant)
Large scale
(nonlinear multivariable)
Uncertainty Small Large
Control Dynamics-driven
(continuous, instinct)
Knowledge-driven
(discrete, logic)
Evaluation Accuracy/precision Profit
Intelligence Low
(adaptation)
High
(decision)
Tab.1  Multiscale properties of the manufacturing industry.
Fig.1  Functional layers for uncertainty processing.
Fig.2  The framework of space-time separation method. BF: basis function.
Fig.3  Methodology for integrated design and control.
Fig.4  A pyramid of intelligent methods.
Fig.5  Probabilistic-fuzzy modeling.
Fig.6  A jet dispensing system for electronics packaging.
Fig.7  The dual-scale property of the jeting process.
Fig.8  A simulation of the jetting process (unit: mm).
Fig.9  Cross-scale multivariable compensation. ηr: viscosity setpoint (required value); Tr: temperature setpoint (required value); Vr: pressure setpoint (required value); V : average of volume in batch; Pc: pressure output from controller; Tm: measured temperature; uT: output signal from temperature controller.
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