Digital twin-enabled Graduation Intelligent Manufacturing System for fixed-position assembly islands

2020 ◽  
Vol 63 ◽  
pp. 101917 ◽  
Author(s):  
Daqiang Guo ◽  
Ray Y. Zhong ◽  
Peng Lin ◽  
Zhongyuan Lyu ◽  
Yiming Rong ◽  
...  
Keyword(s):  
Manufacturing System ◽  
Intelligent Manufacturing ◽  
Fixed Position ◽  
Intelligent Manufacturing System ◽  
Digital Twin

scholarly journals Application Research of Digital Twin-Driven Ship Intelligent Manufacturing System: Pipe Machining Production Line

2021 ◽  
Vol 9 (3) ◽  
pp. 338
Author(s):  
Qingcai Wu ◽  
Yunsheng Mao ◽  
Jianxun Chen ◽  
Chong Wang
Keyword(s):  
Production Line ◽  
Manufacturing System ◽  
Intelligent Manufacturing ◽  
Application Framework ◽  
Application Process ◽  
Layer System ◽  
Intelligent Manufacturing System ◽  
Digital Twin ◽  
Digital Twins ◽  
Implementation Effect

Digital twin has aroused extensive attention of international academia and industry to support future interaction with the physical and virtual world. Although the research and application of digital twin spring up continuously, the concept in the manufacturing domain remains in its infancy. In this context, this paper first reviews the applications of digital twins for intelligent manufacturing. Then it presents an innovative application framework of a digital twin-driven ship intelligent manufacturing system and analyzes its operation mechanism. The application framework of a digital twin-driven ship intelligent manufacturing system mainly includes five parts: the physical layer, model layer, data layer, system layer, and application layer. Finally, key enabling techniques, as well as a case study in a pipe machining production line, are constructed and studied to validate the proposed approach. Meanwhile, system design and implementation, the twin modeling construction, application process, and implementation effect of the pipe machining production line are described in detail to provide a reference for enterprises.

scholarly journals Towards Assembly 4.0: Graduation Intelligent Manufacturing System for Fixed-position Assembly Islands

2019 ◽  
Vol 52 (13) ◽  
pp. 1513-1518 ◽  
Author(s):  
Daqiang Guo ◽  
Peng Lin ◽  
Zhongyuan Lyu ◽  
Shiquan Ling ◽  
Mingxing Li ◽  
...  
Keyword(s):  
Manufacturing System ◽  
Intelligent Manufacturing ◽  
Fixed Position ◽  
Intelligent Manufacturing System

An EDM Multi-Agent Collaborative Manufacturing System Based on Petri Net

2012 ◽  
Vol 457-458 ◽  
pp. 921-926
Author(s):  
Jin Zhi Zhao ◽  
Yuan Tao Liu ◽  
Hui Ying Zhao
Keyword(s):  
Petri Net ◽  
Manufacturing System ◽  
Intelligent Manufacturing ◽  
Agent Technology ◽  
Manufacturing Processes ◽  
Intelligent Manufacturing System ◽  
Collaborative Manufacturing ◽  
Model Based ◽  
Support Organizations ◽  
Multi Agent

A framework for building EDM collaborative manufacturing system using multi-agent technology to support organizations characterized by physically distributed, enterprise-wide, heterogeneous intelligent manufacturing system over Internet is proposed. According to the characteristics of agile EDM collaborative manufacturing system(AEDMCMS), the agent technology is combined with Petri net in order to analyze the model. Based on the basic Petri Net, the definition is extended and the Agent-oriented Petri net (APN) is proposed. AEDMCM is turned into the model of Petri Net which is suitable to the analysis and optimization of manufacturing processes.

Design and Algorithm of Intelligent Manufacturing Evaluation Model Based on Machine Learning

2021 ◽  
Author(s):  
Xianwang Li ◽  
Zhongxiang Huang ◽  
Wenhui Ning
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Manufacturing System ◽  
Learning Algorithm ◽  
Intelligent Manufacturing ◽  
System Model ◽  
Machine Learning Algorithm ◽  
Model Design ◽  
Bp Network ◽  
Intelligent Manufacturing System

Abstract Machine learning is gradually developed and applied to more and more fields. Intelligent manufacturing system is also an important system model that many companies and enterprises are designing and implementing. The purpose of this study is to evaluate and analyze the model design of Intelligent Manufacturing System Based on machine learning algorithm. The method of this study is to first obtain all the relevant attributes of the intelligent manufacturing system model, and then use machine learning algorithm to delete irrelevant attributes to prevent redundancy and deviation of neural network fitting, make the original probability distribution as close as possible to the distribution when using the selected attributes, and use the ratio of industry average to quantitative expression for measurable and obvious data indicators. As a result, the average running time of the intelligent manufacturing system is 17.35 seconds, and the genetic algorithm occupies 15.63 seconds. The machine learning network takes up 1.72 seconds. Under the machine learning algorithm, the training speed is very high, obviously higher than that of the genetic algorithm, and the BP network is 2.1% higher than the Elman algorithm. The evaluation running speed of the system model design is fast and the accuracy is high. This study provides a certain value for the model design evaluation and algorithm of various systems in the intelligent era.

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