Development of Digital Twin with External Data Resources in Manufacturing with Complex Algorithms

被引:0
|
作者
Vijayalakshmi N. [1 ]
Roopa Y.M. [2 ]
Ashreetha B. [3 ]
Ramesh J.V.N. [4 ]
Rao E.G. [5 ]
Sundararajan P.N. [6 ]
机构
[1] Department of Computer Science (MCA), Mount Carmel College, Autonomous, No.58, Palace Road, Bengaluru
[2] Department of CSE, Institute of Aeronautical Engineering, Telangana, Hyderabad
[3] Department of Electronics & Communication Engineering College, School of Engineering & Technology, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Andhra Pradesh, Tirupati
[4] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur District, AP, Vaddeswaram
[5] Department of Information Technology, MLR Institute of Technology, Hyderabad
[6] Department of ECE, PSNA College of Engineering and Technology, Dindigul
关键词
BIM; Complex algorithm; Digital twin; Engineering problems; Manufacturing;
D O I
10.1007/s42979-023-02035-1
中图分类号
学科分类号
摘要
Although computational methods have historically been utilized by architecture companies, they are now becoming more and more common in popular constructions for automated process Digital Twin (DT) design, as-built assessment, and parametric simulation techniques. This study shows how workflow might well be utilized to converge on optimum and heuristic approaches to difficult issues and how optimization algorithms have the potential to help smart technology in assembly. In this study, two kinds were studied. The first involves a computational technique that has been applied to a difficult geometric problem in the manufacture of structural sheathing panels. The following illustration shows a computational process consisting of methods to automate the extraction and processing of Building Information Model (BIM) information for the output path shows significance. Learning from these examples shows how computer simulation should be effective for complex and repeated process development. Here, it must be demonstrated that computational techniques can effectively search for large-choice regions and detect costly development errors from the outset. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
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