Intelligence Monitor and Diagnosis to High Speed Brushes Aeration Mechanics Based on Turbulent Flow Displacement Sensing Theory

2010 ◽  
Vol 426-427 ◽  
pp. 191-196
Author(s):  
H.I. Liu ◽  
X.P. Li ◽  
Yan Nian Rui ◽  
Ying Ping He
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Eddy Current ◽  
High Speed ◽  
Current Effect ◽  
Vibration Signals ◽  
Flow Displacement ◽  
Eddy Current Effect ◽  
Artificial Neural ◽  
Main Spindle

High Speed Brushes Aeration Mechanics are the effective aeration equipments which are widely used in the environmental protection. Because of the big span of main spindle and its high speed when it is working, the breakdown sometimes occurs. It is very importance to monitor its condition and diagnose its breakdowns. Turbulent Flow Displacement Sensors are the non-contact types which are based on eddy current effect. It has many advantages, such as good linearity, wide frequency response scope, convenience installment and so on. So it is very suitable for the main spindle’s vibration signals of a high speed brushes aeration mechanic are monitored. With the development of Artificial Neural Networks technology, the equipment breakdown diagnosis has realized intellectualization. The recognition of equipment failure types is one of the most important studying domains of Artificial Neural Networks at present. Based on the research of eddy current effect and Artificial Neural Networks, we build up a test system which can monitor condition and diagnose breakdown to a GSB-12 high speed brushes aeration mechanic. With the help of it, the vibration signals of the measurement points on the main spindle are measured at two mutually vertical positions. The signals’ base frequency and multiplicative frequency are taken as characteristic value. Six common breakdowns are selected and to be taken as the standard sample and there are 3 lays in the neural network. Using FBP algorithm, we get a satisfied effect. The experiment has confirmed that this method is advanced, reliable and practical. It provides a new method about intelligent monitor and breakdown diagnosis to high speed brushes aeration mechanics’ condition.

Design and indirect adaptive fuzzy H∞ control of a novel retarder coupled with eddy current effect and MR effect

2020 ◽  
pp. 1045389X2096318
Author(s):  
Xiaoqin Xu ◽  
Tianyu Zhu ◽  
Shumei Chen ◽  
Cheng Wang
Keyword(s):  
Eddy Current ◽  
High Speed ◽  
Control Process ◽  
Constant Speed ◽  
Current Effect ◽  
Adaptive Fuzzy ◽  
Eddy Current Effect ◽  
Full Speed ◽  
Braking Process ◽  
Mr Effect

To improve the poor braking performance of the traditional eddy current retarder, a novel retarder is developed by coupling the eddy current effect and the magnetorheological effect, which has large torque at low speed and stable torque at high speed. To meet the constant speed requirement in the braking process, the indirect adaptive fuzzy H∞ control strategy is employed to control the rotor speed of the designed retarder. The results show that the proposed retarder has the characteristics of continuously adjustable large torque in full speed section, sensitive control process and constant speed intelligent control.

scholarly journals Modeling Cutting Forces in High-Speed Turning using Artificial Neural Networks

TecnoLógicas ◽  
2021 ◽  
Vol 24 (51) ◽  
pp. e1671
Author(s):  
Luis W. Hernández-González ◽  
Dagnier A. Curra-Sosa ◽  
Roberto Pérez-Rodríguez ◽  
Patricia D.C. Zambrano-Robledo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Cutting Forces ◽  
High Speed ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Machining Time ◽  
High Speed Turning ◽  
Artificial Neural

Cutting forces are very important variables in machining performance because they affect surface roughness, cutting tool life, and energy consumption. Reducing electrical energy consumption in manufacturing processes not only provides economic benefits to manufacturers but also improves their environmental performance. Many factors, such as cutting tool material, cutting speed, and machining time, have an impact on cutting forces and energy consumption. Recently, many studies have investigated the energy consumption of machine tools; however, only a few have examined high-speed turning of plain carbon steel. This paper seeks to analyze the effects of cutting tool materials and cutting speed on cutting forces and Specific Energy Consumption (SEC) during dry high-speed turning of AISI 1045 steel. For this purpose, cutting forces were experimentally measured and compared with estimates of predictive models developed using polynomial regression and artificial neural networks. The resulting models were evaluated based on two performance metrics: coefficient of determination and root mean square error. According to the results, the polynomial models did not reach 70 % in the representation of the variability of the data. The cutting speed and machining time associated with the highest and lowest SEC of CT5015-P10 and GC4225-P25 inserts were calculated. The lowest SEC values of these cutting tools were obtained at a medium cutting speed. Also, the SEC of the GC4225 insert was found to be higher than that of the CT5015 tool.

A Review: Artificial Neural Network

2018 ◽  
Vol 8 (04) ◽  
Author(s):  
Jaspreet Kaur ◽  
Prabhpreet Kaur
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
High Speed ◽  
Parallel Architecture ◽  
Research Area ◽  
Neural Nets ◽  
Optimization Function ◽  
Artificial Neural

Neural networks are those information processing systems, which are built and performed to design the human brain. The main objective of the neural network research is to evolve a computational device for representing the brain to perform various evaluating tasks at a faster rate than the traditional systems. Neural networks are latest method of programming computers. Several programs that utilize neural nets are also proficient Neural networks have appeared in the past few years as an area of different opportunity for research area, development and application to a variety of real world problems because of their rapid feedback and parallel architecture. Artificial neural networks perform various tasks such as pattern-matching and classification, optimization function and data clustering. These tasks are very difficult for traditional for implementation of artificial neural networks, high-speed digital computers are used, which makes the simulation if neural processes feasible. This paper provides a broad overview of the wide array of artificial neural networks, some of the most commonly network architecture and various learning processes currently in use in research. Also concisely describes several applications of it.

Prediction of Surface Roughness in High Speed Milling Process Using the Artificial Neural Networks

2007 ◽  
Vol 364-366 ◽  
pp. 713-718 ◽  
Author(s):  
Dong Woo Kim ◽  
Young Jae Shin ◽  
Kyoung Taik Park ◽  
Eung Sug Lee ◽  
Jong Hyun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
High Speed ◽  
Back Propagation Algorithm ◽  
High Speed Milling ◽  
Artificial Neural ◽  
Cutting Path

The objective of this research was to apply the artificial neural network algorithm to predict the surface roughness in high speed milling operation. Tool length, feed rate, spindle speed, cutting path interval and run-out were used as five input neurons; and artificial neural networks model based on back-propagation algorithm was developed to predict the output neuron-surface roughness. A series of experiments was performed, and the results were estimated. The experimental results showed that the applied artificial neural network surface roughness prediction gave good accuracy in predicting the surface roughness under a variety of combinations of cutting conditions.

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