Feature-based modeling for industrial processes in the context of digital twins: A case study of HVOF process

Ren, Jiangzhuo; Zhou, Tianyu; Ye, Rong; Ma, Yongsheng; Ahmad, Rafiq

doi:10.1016/j.aei.2021.101486

Cited by 12 publications

(5 citation statements)

References 45 publications

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“…From the perspective of the HVOF system design and parameter selection, it was of great significance to establish a digital simulation model that could reflect the characteristics of the actual equipment so that designers or users could quickly obtain the required parameter information before operating the engineering prototype [21].…”

Section: The Digital Modelmentioning

confidence: 99%

“…Because the concept of digital twins is very popular in the industrial field, the digital model has recently provided an extremely convenient and valuable approach for the analysis and improvement research of the spray process [20,21]. The current simulation research of the HVOF spray system mainly focuses on the classical spray flow field [22,23], the particle deposition process [24,25], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the inlet boundary conditions of gas flow rate, pressure and particle velocity in the abovementioned, time-consuming models usually lack the simulation data from the source and are mostly defined subjectively, which greatly affect the accuracy of the spray process. A system simulation digital model reflecting the whole state of the spray equipment can solve the difficulty theoretically and will provide an effective method of engineering prototype designs and spray process predictions [21]. In fact, the HVOF spray system works essentially as a small liquid rocket engine (LRE).…”

Section: Introductionmentioning

confidence: 99%

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Characteristic Analysis and Coating Application of the Innovative HVOF System Based on the Digital Model

Yan,

Yuan,

et al. 2024

Processes

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In view of the poor working conditions, high cost and time-consuming parameter design of the traditional spray process, an innovative HVOF thermal spray system based on the digital model has been established by this study to improve coating performance and optimize scheme design rapidly. In particular, the digital model of the oxygen/kerosene HVOF spray system is designed on the AMESim multidisciplinary simulation platform for the first time, and the engineering prototype has been successfully developed. Thus, an efficient design method based on the digital model was proposed, according to which the spray control parameters such as oxygen and kerosene flow are obtained conveniently under a combustion chamber pressure of 1.0 MPa and 2.0 MPa, respectively. The error between the simulation and experiment results was generally less than 5%, and the dynamic characteristics of the key components in the actual spray system were well predicted, suggesting that the dynamic response time of the system would generally less than 0.7 s. Additionally, the WC-12Co coatings were deposited under the working conditions of W1.0 and W2.0, respectively, the microhardness of the coating increased about 23% and the corresponding volume wear rate decreased about 18%. The results show that the increase of the pressure of the combustion chamber can further improve the coating performance, which also verifies the feasibility and reliability of the design method. It was concluded that the innovative HVOF system based on the digital model is of great theoretical value and application significance for predicting spray process parameters conveniently and providing excellent coating performance.

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Section: The Digital Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characteristic Analysis and Coating Application of the Innovative HVOF System Based on the Digital Model

Yan,

Yuan,

et al. 2024

Processes

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“…In turn, methods to accurately solve PDEs are of great significance. In complex industrial systems, such as digital twin and parallel control [24,36], PDE provides relevant models to lay the foundation of the whole process. Currently, besides analytical methods, the solution of a PDE may be obtained by finite difference, finite volume, and finite element methods (FEM), as well as other numerical methods.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

Shi¹,

Liu²,

null³

et al. 2023

NMTMA

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Physics-Informed Neural Network (PINN) represents a new approach to solve Partial Differential Equations (PDEs). PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions (I/BCs) into a loss function. However, the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge, e.g. because they fall into local optima. In other words, the presence of balanced PDE loss, initial loss and boundary loss may be critical for the convergence. In addition, existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the boundaries. Overall, these problems have made PINN-based methods of limited use on practical situations. In this paper, we propose a novel physics-informed neural network, i.e. an adaptive physics-informed neural network with a two-stage training process. Our algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function, and solve PDEs using a two-stage training process: pre-training and formal training. The pre-training step ensures the convergence of boundary loss, whereas the formal training process completes the solution of PDE by balancing various loss functions. In order to verify the performance of our method, we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical situations. The Klein-Gordon equation, which is widely used to compare performance, reveals that our method is able to reduce the hidden errors. Experimental results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function, hidden errors and conduction errors.

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“…Numerous excellent cases have greatly promoted the development of digital twin technology [21][22][23][24][25][26]. However, existing research mostly focuses on the innovation of digital twin frameworks and concepts, and digital twin modeling techniques are still a hot and difficult research area in the field of smart manufacturing [27].…”

Section: Introductionmentioning

confidence: 99%

Digital Twin-Based Vibration Monitoring of Plant Factory Transplanting Machine

Chen,

Zhao,

Zhang

et al. 2023

Applied Sciences

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In response to the problem of bowl seedling detachment caused by the shaking of the transplanting machine in plant factories, this paper proposes a physical entity monitoring method for the digital twin (DT) plant factory transplanting system. The method is used to analyze the vibration signals of the transplanting machine under different operating conditions and explore the optimal working conditions. Firstly, a demand analysis for the physical entity of the DT plant factory transplanting system is conducted, focusing on practical applications. Then, an optimal deployment plan is designed based on the axiomatic design (AD) theory. Subsequently, a comparative analysis of the operating conditions of the plant factory transplanting equipment is carried out using data-driven approaches. Finally, the optimal working condition parameters are determined by comparing the modal vibration power spectral density of the transplanting equipment under different operating conditions. The results show that the maximum amplitude occurs in the Z-axis, with a magnitude of 2.1 m/s2. By comparing the cloud maps, it is evident that the vibration trends in the Z-axis and X-axis above the transplanting robotic arm are more pronounced compared to the Y-axis. This indicates that under the operating condition of transplanting 3000 plants per hour, a high transplanting efficiency can be maintained, and the vibration signals in the XYZ-axis above the transplanting robotic arm are relatively smooth, making them suitable for transplanting operations. This study combines digital twin technology to analyze the vibration signals of the plant factory transplanting machine under different operating conditions and explore the optimal working conditions. Compared to traditional monitoring platforms, this method facilitates the real-time visualization of different operating conditions of the transplanting machine in a virtual mapping, providing a more intuitive reflection of the equipment operation status.

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Feature-based modeling for industrial processes in the context of digital twins: A case study of HVOF process

Cited by 12 publications

References 45 publications

Characteristic Analysis and Coating Application of the Innovative HVOF System Based on the Digital Model

Characteristic Analysis and Coating Application of the Innovative HVOF System Based on the Digital Model

An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

Digital Twin-Based Vibration Monitoring of Plant Factory Transplanting Machine

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