A cloud-based digital twin for monitoring of an adaptive clamping mechanism used for high performance composite machining

Weckx, Sam; Robyns, Steven; Baake, Jonathan; Kikken, Edward; Geest, Roeland De; Birem, Merwan; Maes, Davy

doi:10.1016/j.procs.2022.01.221

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Through experiments considering the full-factor information of the clamping system, they proved the effectiveness of the method. Weckx et al [162] proposed a cloudbased DT for monitoring high-performance composite machining adaptive clamping devices by incorporating computer vision-related technology. This implementation achieved functions such as tool wear monitoring based on the clamping force and the evaluation of clamping device operation.…”

Section: Applications In Machiningmentioning

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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Section: Applications In Machiningmentioning

confidence: 99%

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Hu,

Li,

et al. 2024

Intelligent and Sustainable Manufacturing

View full text Add to dashboard Cite

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“…The surface smoothness check is obtained through the built-in lighting and curvature check methods of UG software. Previous studies have focused on local path optimization, without approaching the envelope surface and transforming the tool path planning problem into a tool path optimization problem under a single tool path (Ladj et al, 2021;Weckx et al, 2022). This study will take side milling as an example for analysis, and the definitions of tool position, design surface, envelope surface, and blade offset surface are as follows.…”

Section: S U V ( )mentioning

confidence: 99%

Optimization of machining path for integral impeller side milling based on SA-PSO fusion algorithm in CNC machine tools

Zhao

2024

Front. Mech. Eng.

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The five axis linkage Computer Numerical Control machine tool for integral impeller can achieve blade machining through side milling, which is of great significance for improving the machining accuracy, production efficiency, and long-term stability of integral impeller blades. This study is based on non-uniform rational B-spline curves and aims to reduce the surface over cutting or under cutting of integral turbine blades. The path planning of non deployable ruled surfaces was analyzed in depth through side milling, and the path planning model of the side milling cutter axis was solved through a fusion algorithm of simulated annealing algorithm and particle swarm optimization algorithm, in order to find the optimal path through iterative process. As the number of iterations increased, the error values of particle swarm optimization algorithm and simulated annealing particle swarm optimization fusion algorithm gradually decreased, with convergence times of about 7 and 6, respectively. The stable error value of the fusion algorithm was 0.253, which is 30.45% lower than that of the particle swarm optimization algorithm. The optimal number of iterations for solving the model using particle swarm optimization algorithm and fusion algorithm was the 7th, with range values of 0.0213 and 0.0165 mm, respectively. The tool axis trajectory surface optimized by the fusion algorithm was closer to the tool axis motion state compared to the initial tool axis trajectory surface. The range of the sum of mean squared deviations for single and global cutting was 0.0011–0.0198 and 0.046–0.0341, but the overall error value was relatively small. This study effectively reduces the envelope error of machining tools and improves machining accuracy, thereby solving the principle error of non expandable ruled surfaces in the motion trajectory of the blade axis of the integral turbine. This provides new research ideas for the intelligent development of Computer Numerical Control machining technology.

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“…It is shown how a digital twin for machine tool monitoring can be made by means of a commercial cloud computing platform like Microsoft Azure, and the selection of the different components of the cloud architecture are being discussed. This paper is an extension of the work presented in [34]. The paper is structured as follows.…”

Section: Paper Contributions and Outlinementioning

confidence: 99%

Development and deployment of a digital twin for monitoring of an adaptive clamping mechanism, used for high performance composite machining

Weckx

Meyers

Jordens

et al. 2022

IET Collab Intel Manufact

Self Cite

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In this work, we present a cloud‐based digital twin for monitoring of a clamping technology for machining of composite parts. Supporting large and/or freeform composite parts is crucial to avoid bending during drilling. Bending of the part will lead to delamination and frayed edges of the drilled holes. The new active clamping technology allows to realise a stabilised fixture, localised in the area where the drilling occurs, to avoid bending. This significantly improves the quality of the drilled holes. The clamping device is equipped with an IoT edge device, with a bidirectional communication to the cloud. The cloud‐based digital twin analyses the quality of the drilled holes based on computer vision, monitors the drill wear and detects incorrect operation of the active clamping device. All data is stored in the cloud. By means of a knowledge graph, which acquires and integrates information into an ontology and provides a central information access, it will be easier for a data scientist to query this data and to gain new insights in the operation of the drill with active clamping device. The full deployment occurs on the Microsoft Azure cloud platform. This transforms the standard machine into an Industry 4.0 compliant machine.

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A cloud-based digital twin for monitoring of an adaptive clamping mechanism used for high performance composite machining

Cited by 8 publications

References 15 publications

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Optimization of machining path for integral impeller side milling based on SA-PSO fusion algorithm in CNC machine tools

Development and deployment of a digital twin for monitoring of an adaptive clamping mechanism, used for high performance composite machining

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