scholarly journals Quality Prediction and Control of Assembly and Welding Process for Ship Group Product Based on Digital Twin

Scanning ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Lei Li ◽  
Di Liu ◽  
Jinfeng Liu ◽  
Hong-gen Zhou ◽  
Jiasheng Zhou
Keyword(s):  
Quality Control ◽  
Welding Process ◽  
Quality Prediction ◽  
Twin Data ◽  
Time Data ◽  
Digital Twin ◽  
Twin Model ◽  
Prediction And Control ◽  
And Control ◽  
Group Product

In view of the problems of lagging and poor predictability for ship assembly and welding quality control, the digital twin technology is applied to realize the quality prediction and control of ship group product. Based on the analysis of internal and external quality factors, a digital twin-based quality prediction and control process was proposed. Furthermore, the digital twin model of quality prediction and control was established, including physical assembly and welding entity, virtual assembly and welding model, the quality prediction and control system, and twin data. Next, the real-time data collection based on the Internet of Things and the twin data organization based on XML were used to create a virtual-real mapping mechanism. Then, the machine learning technology is applied to predict the process quality of ship group products. Finally, a small group is taken as an example to verify the proposed method. The results show that the established prediction model can accurately evaluate the welding angular deformation of group products and also provide a new idea for the quality control of shipbuilding.

Quality Measure and Control System for the Whole Process of Arc Welding

2008 ◽  
Vol 575-578 ◽  
pp. 722-727
Author(s):  
Zhi Yong Li ◽  
Bao Wang ◽  
Jing Bin Ding
Keyword(s):  
Quality Control ◽  
Control System ◽  
Welding Process ◽  
Quality Measure ◽  
Welding Quality ◽  
Welding Material ◽  
Whole Process ◽  
Metallurgical Structure ◽  
Measure And Control System ◽  
And Control

Welding quality control is critical for welding manufacturing. However, the factors that affect welding quality exist in the whole welding process. Whole process welding quality control is a technology control process that can control the welding quality from choice of the welding material, stability of the welding process and quality assurance after welding. In this paper, a quality measure and control system is developed. The system is consisted of three modules: Technology property evaluation and welding material choice module is based on Analysator Hannover. This module can evaluate three type of welding material: electrode, solid welding wire and flux-cored wire. The welding process stability evaluation module can collect electrical and light spectral signal for disturbance factors diagnosis during the welding. The metallurgical structure and property forecasting module call the CCT diagram data base for predicting he metallurgical structure and mechanical property of the weld affected zone and weld metal. For stainless steel, a predicting method based on Schaeffer is also provided in this module.

Quality prediction and control of tube hollow

2011 ◽  
Vol 18 (3) ◽  
pp. 767-772
Author(s):  
Dong Xiao ◽  
Ji-chun Wang ◽  
Xiao-li Pan ◽  
Zhi-zhong Mao
Keyword(s):  
Quality Prediction ◽  
Prediction And Control ◽  
And Control

scholarly journals Real-time data assimilation and control on mechanical systems under uncertainties

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Paul-Baptiste Rubio ◽  
Ludovic Chamoin ◽  
François Louf
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Feedback Loop ◽  
Model Updating ◽  
Research Work ◽  
Welding Process ◽  
Fusion Welding ◽  
Time Data ◽  
Application Systems ◽  
And Control

AbstractThis research work deals with the implementation of so-called Dynamic Data-Driven Application Systems (DDDAS) in structural mechanics activities. It aims at designing a real-time numerical feedback loop between a physical system of interest and its numerical simulator, so that (i) the simulation model is dynamically updated from sequential and in situ observations on the system; (ii) the system is appropriately driven and controlled in service using predictions given by the simulator. In order to build such a feedback loop and take various uncertainties into account, a suitable stochastic framework is considered for both data assimilation and control, with the propagation of these uncertainties from model updating up to command synthesis by using a specific and attractive sampling technique. Furthermore, reduced order modeling based on the Proper Generalized Decomposition (PGD) technique is used all along the process in order to reach the real-time constraint. This permits fast multi-query evaluations and predictions, by means of the parametrized physics-based model, in the online phase of the feedback loop. The control of a fusion welding process under various scenarios is considered to illustrate the proposed methodology and to assess the performance of the associated numerical architecture.

Modeling and Control of Real Flow Calibration System

2020 ◽  
Author(s):  
Kai Wen
Keyword(s):  
Process Control ◽  
Calibration System ◽  
Process Data ◽  
Flow Meters ◽  
Digital Twin ◽  
Twin Model ◽  
Scada System ◽  
Human Operators ◽  
Real Flow ◽  
And Control

Abstract The calibration of large-diameter flow meters is performed in the calibration station where real flow passes through. The typical calibration process is manipulated by human operators, which is time-consuming and easily affected. Since most of the process parameters are detectable, the smart calibration system was aided by the on-line modeling process and consisted of three parts: the digital twin model, the process controller, and the human-machine interface (HMI). The digital twin model was based on the basic partial differential equations of the gas flow in pipelines and was meant for the flow behavior prediction over short periods and provided decision-making assistance for human operators. The verification of the digital model was based on both the historical process data and the real-time process data. The process controller represented the manipulator meant to replace the human operator. The function of the controller included process control and calibration flow point adjustment. The HMI was designed based on the industrial supervisory control and data acquisition (SCADA) system. Since the process control was essential, the scheduling scheme and command sequence feedback to the SCADA system was rechecked by human operators via the HMI. The result of the active control was displayed in the HMI based on the digital twin model. Since smart control was the tendency in the piping system, the automated process verification and control formed the basis of the smart system. By entering the size and range of the flow meters into the HMI, the entire industrial system inside the calibration station was executed automatically.

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