Synchronization of production and delivery with time windows in fixed-position assembly islands under Graduation Intelligent Manufacturing System

2022 ◽  
Vol 73 ◽  
pp. 102236
Author(s):  
Daqiang Guo ◽  
Zhongyuan Lyu ◽  
Wei Wu ◽  
Ray Y. Zhong ◽  
Yiming Rong ◽  
...  
Keyword(s):  
Manufacturing System ◽  
Time Windows ◽  
Intelligent Manufacturing ◽  
Fixed Position ◽  
Intelligent Manufacturing System

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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