scholarly journals Optimal control of a two-stage hybrid manufacturing system model

Author(s):  
C.G. Cassandras ◽  
Qinjia Liu ◽  
K. Gokbayrak ◽  
D.L. Pepyne
Author(s):  
Lan Ren ◽  
Kunnayut Eiamsa-ard ◽  
Jianzhong Ruan ◽  
Frank Liou

At present, part remanufacturing technology is gaining more interest from the military and industries due to the benefits of cost reduction as well as time and energy savings. This paper presents the research on one main component of part remanufacturing technology, which is part repairing. Traditionally, part repairing is done in the repair department using welding processes. However, the limitations of the traditional welding process are becoming more and more noticeable when accuracy and reliability are required. Part repairing strategies have been developed utilizing a hybrid manufacturing system in which the laser-aided deposition and CNC cutting processes are integrated. Part repairing software is developed in order to facilitate the users. The system and the software elevate the repairing process to the next level, in which accuracy, reliability, and efficiency can be achieved. The concept of the repairing process is presented in this paper, and verification and experimental results are also discussed.


2021 ◽  
Author(s):  
Xianwang Li ◽  
Zhongxiang Huang ◽  
Wenhui Ning

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.


Author(s):  
P S Murdapa ◽  
I N Pujawan ◽  
P D Karningsih ◽  
A H Nasution

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