The process correlation interaction construction of Digital Twin for dynamic characteristics of machine tool structures with multi-dimensional variables

Liang, Zhaoshun; Wang, Zhen; Peng, Yili; Mao, Xinyong; Xing, Yuan; Yang, Aodi; Yin, Ling

doi:10.1016/j.jmsy.2022.03.002

Cited by 28 publications

(6 citation statements)

References 58 publications

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“…This algorithm achieves efficient search of high-quality solution sets through a non-sorted survival selection mechanism and crowding distance sorting [19]. However, integrating it into specific manufacturing environments requires a deep understanding of the characteristics of manufacturing systems to design optimization strategies that meet practical needs [20]. Firstly, it discusses the optimization scheduling objectives for the workshop.…”

Section: B Construction and Optimization Of Nsga-ii For Dtmssmentioning

confidence: 99%

Optimization Method for Digital Twin Manufacturing System Based on NSGA-II

Ding,

2024

IJACSA

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In the wave of industrial modernization, a concept that comprehensively covers the product lifecycle has been proposed, namely the digital twin manufacturing system. The digital twin manufacturing system can conduct three-dimensional simulation of the workshop, thereby achieving dynamic scheduling and energy efficiency optimization of the workshop. The optimization of digital twin manufacturing systems has become a focus of research. In order to reduce power consumption and production time in manufacturing workshops, the study adopted a non-dominated sorting genetic algorithm to improve its elitist retention strategy for the problem of easily falling into local optima. On the ground of the idea of multi-objective optimization, the optimization was carried out with the production time and power consumption of the manufacturing industry as the objectives. The experiment showcased that the improved algorithm outperforms the multi-objective optimization algorithm on the ground of decomposition and the evolutionary algorithm on the ground of Pareto dominance. Compared to the two comparison algorithms, the production time optimization effect and power consumption optimization effect of different numbers of devices were 11.12%-21.37% and 2.14%-6.89% higher, respectively. The optimization time of the improved algorithm was 713.5 seconds, that was reduced by 173.8 seconds and 179.8 seconds compared to the other two algorithms, respectively. The total power consumption of the improved optimization model was 2883.7kWs, which was reduced by 32.0kW*s and 45.5kW*s compared to the other two algorithms, respectively. This study proposed a new multi-objective optimization algorithm for the current digital twin manufacturing industry. This algorithm effectively reduces production time and power consumption, and has important guiding significance for manufacturing system optimization in actual production environments.

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Section: B Construction and Optimization Of Nsga-ii For Dtmssmentioning

confidence: 99%

Optimization Method for Digital Twin Manufacturing System Based on NSGA-II

Ding,

2024

IJACSA

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“…This approach facilitated health monitoring, fault detection, and degradation tracking of rotating machinery without the need for prior knowledge, historical data, or fault samples. In a similar vein, Liang et al [98] proposed a multidynamic process modeling model using the DT framework. They established a system-oriented correlation and interaction mechanism to optimize cutting parameters, visualize process variables, and assess machining stability.…”

Section: Model Constructionmentioning

confidence: 99%

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Hu,

Li,

et al. 2024

Intelligent and Sustainable Manufacturing

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Digital twin technology develops virtual models of objects digitally, simulating their real-world behavior based on data. It aims to reduce product development cycles and costs through feedback between the virtual and real worlds, data fusion analysis, and iterative decision-making optimization. Traditional manufacturing processes often face challenges such as poor real-time monitoring and interaction during machining, difficulties in diagnosing equipment failures, and significant errors in machining. Digital twin technology offers a powerful solution to these issues. Initially, a comprehensive review of the research literature was conducted to assess the current research scope and trends. This was followed by an explanation of the basic concepts of digital twins and the technical pathway for integrating digital twins into intelligent manufacturing including outlining the essential technologies for creating a system of interaction between the virtual and real worlds, enabling multimodel fusion, data sensing, algorithm-based prediction, and intelligent decision-making. Moreover, the application of digital twins in intelligent manufacturing throughout the product life cycle was detailed, covering product design, manufacturing, and service stages. Specifically, in the manufacturing phase, a model based on heat conduction theory and visualization was used to construct a time-varying error model for the motion axis, leading to experiments predicting the time-varying error in the hole spacing of a workpiece. These experiments achieved a minimum prediction error of only 0.2 μm compared to the actual error. By compensating for time-varying errors in real time, the variability in the hole spacing error decreased by 69.19%. This paper concludes by summarizing the current state of digital twins in intelligent manufacturing and projecting future trends in key technologies, application areas, and data use, providing a basis for further research.

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“…On this basis, an application framework of DT in product life cycle management was proposed. In the new digital era, all kinds of things and even the whole world can be digitized as models [70][71][72]. Simulations were conducted for a variety of purposes, and the results can provide intelligent support for various decisions.…”

Section: Dts Modeling Contributes To Intelligent Manufacturingmentioning

confidence: 99%

Modeling Methods of 3D Model in Digital Twins

Liu¹,

Li²,

Lv³

2023

Computer Modeling in Engineering &Amp; Sciences

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To understand the current application and development of 3D modeling in Digital Twins (DTs), abundant literatures on DTs and 3D modeling are investigated by means of literature review. The transition process from 3D modeling to DTs modeling is analyzed, as well as the current application of DTs modeling in various industries. The application of 3D DTs modeling in the elds of smart manufacturing, smart ecology, smart transportation, and smart buildings in smart cities is analyzed in detail, and the current limitations are summarized. It is found that the 3D modeling technology in DTs has broad prospects for development and has a huge impact on all walks of life and even human lifestyles. At the same time, the development of DTs modeling relies on the development and support capabilities of mature technologies such as Big Data, Internet of Things, Cloud Computing, Arti cial Intelligence, and game technology. Therefore, although some results have been achieved, there are still limitations. This work aims to provide a good theoretical support for the further development of 3D DTs modeling.

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The process correlation interaction construction of Digital Twin for dynamic characteristics of machine tool structures with multi-dimensional variables

Cited by 28 publications

References 58 publications

Optimization Method for Digital Twin Manufacturing System Based on NSGA-II

Optimization Method for Digital Twin Manufacturing System Based on NSGA-II

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Modeling Methods of 3D Model in Digital Twins

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