On-line construction of dynamic control system in intelligent manufacturing

2021 ◽  
Author(s):  
Hua Liu ◽  
quanxin Ding ◽  
Ronggang ZHU
Keyword(s):  
Control System ◽  
Dynamic Control ◽  
Intelligent Manufacturing ◽  
On Line ◽  
Dynamic Control System ◽  
Line Construction

An integrated feature-based dynamic control system for on-line machining, inspection and monitoring

2015 ◽  
Vol 22 (2) ◽  
pp. 187-200 ◽  
Author(s):  
Yingguang Li ◽  
Changqing Liu ◽  
James X. Gao ◽  
Weiming Shen
Keyword(s):  
Control System ◽  
Dynamic Control ◽  
On Line ◽  
Feature Based ◽  
Dynamic Control System

612 Experimental PDCS (Passive Dynamic Control System) with MRF-Brake and Spring Balancer

2005 ◽  
Vol 2005.58 (0) ◽  
pp. 225-226
Author(s):  
Naoki NODA ◽  
Yoshihiro UJIMOTO ◽  
Yasuhiro MINAMIYAMA ◽  
Takumi SASAKI ◽  
Takanori KIYOTA ◽  
...  
Keyword(s):  
Control System ◽  
Dynamic Control ◽  
Dynamic Control System

Dynamic On-Line Control of Stainless Steel Making in AOD

2013 ◽  
Vol 794 ◽  
pp. 50-62
Author(s):  
Bahma Deo ◽  
Satish Kumar
Keyword(s):  
Control System ◽  
Heat Balance ◽  
Dynamic Models ◽  
Dynamic Control ◽  
Shop Floor ◽  
Dynamic Adjustment ◽  
Reduction Stage ◽  
Model Based ◽  
Level Ii ◽  
On Line

A new dynamic control model based on simultaneous mass transfer of C, Cr, and Mn and dynamic heat balance is developed. It allows dynamic adjustment of argon-oxygen ratio. The model is implemented through Level II control system. The total operating period of one heat is divided into five different stages: charge calculation, first blow period, second blow period, third blow period and, lastly, the reduction stage. The charge calculation model, based on heat balance, mass balance and the costs decided the optimum charge mix to start with. Both linear and non-linear regression models are used to predict the temperature and composition of bath at the end of first blow period. The second blow and the third blow periods use the dynamic models for predicting the chemical composition and temperature. In the model for reduction stage (final stage) the amount of reduction mixture is determined for obtaining maximum recovery of Cr and Mn from slag. On-line testing of the dynamic models was carried out on the shop floor. The integration of models with the Level II control system using Supervisory Control and Data Acquisition System (SCADA) are discussed. User friendly HMI are developed such that the operators can easily use it during the regular operation on shop-floor. This is the first time that a full dynamic control system was implemented in India for the AOD process.

Implementation of a Dynamic Control System for SCARA Robot

1997 ◽  
Vol 30 (1) ◽  
pp. 297-302
Author(s):  
Jang M. Lee ◽  
M.C. Lee ◽  
K. Son ◽  
M.H. Lee ◽  
S.H . Han
Keyword(s):  
Control System ◽  
Dynamic Control ◽  
Scara Robot ◽  
Dynamic Control System

scholarly journals 50 tons of steel mill BOF steelmaking dynamic control system and the application of variable-frequency emergency power supply-gun incident oxygen converter steel to enhance the development of power

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Hu Weitao
Keyword(s):  
Control System ◽  
Power Supply ◽  
Oxygen Converter ◽  
Dynamic Control ◽  
Converter Steel ◽  
Variable Frequency ◽  
Steel Mill ◽  
Emergency Power ◽  
Dynamic Control System

Abstract: This article emphatically about laigangsteel mills bof dynamic steelmaking control system.Detailed introduces the characteristics of the dynamic steelmaking converter characteristics and its key of technology is described.

scholarly journals Integration of biosensor to a window-based control system for user emotion detection to static and dynamic visual contents of webpages

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatima M. Isiaka ◽  
Awwal Adamu ◽  
Zainab Adamu
Keyword(s):  
Control System ◽  
Eye Movement ◽  
User Experience ◽  
Completion Time ◽  
Dynamic Control ◽  
Task Completion ◽  
Time Interval ◽  
Task Completion Time ◽  
Content Type ◽  
Dynamic Control System

Purpose Basic capturing of emotion on user experience of web applications and browsing is important in many ways. Quite often, online user experience is studied via tangible measures such as task completion time, surveys and comprehensive tests from which data attributes are generated. Prediction of users’ emotion and behaviour in some of these cases depends mostly on task completion time and number of clicks per given time interval. However, such approaches are generally subjective and rely heavily on distributional assumptions making the results prone to recording errors. This paper aims to propose a novel method – a window dynamic control system – that addresses the foregoing issues. Design/methodology/approach Primary data were obtained from laboratory experiments during which 44 volunteers had their synchronized physiological readings – skin conductance response, skin temperature, eye movement behaviour and users activity attributes taken by biosensors. The window-based dynamic control system (PHYCOB I) is integrated to the biosensor which collects secondary data attributes from these synchronized physiological readings and uses them for two purposes: for detection of both optimal emotional responses and users’ stress levels. The method’s novelty derives from its ability to integrate physiological readings and eye movement records to identify hidden correlates on a webpage. Findings The results from the analyses show that the control system detects basic emotions and outperforms other conventional models in terms of both accuracy and reliability, when subjected to model comparison – that is, the average recoverable natural structures for the three models with respect to accuracy and reliability are more consistent within the window-based control system environment than with the conventional methods. Research limitations/implications Graphical simulation and an example scenario are only provided for the control’s system design. Originality/value The novelty of the proposed model is its strained resistance to overfitting and its ability to automatically assess user emotion while dealing with specific web contents. The procedure can be used to predict which contents of webpages cause stress-induced emotions to users.

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