A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective

Ladj, Asma; Wang, Zhiqiang; Meski, Oussama; Belkadi, Farouk; Ritou, Mathieu; Cunha, Catherine da

doi:10.1016/j.jmsy.2020.07.018

Cited by 116 publications

(44 citation statements)

References 41 publications

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“…However, the acquisition of massive, structured and labeled data, especially regarding complex rotating equipment and components, is normally tied up with high costs since the fault-free operation is a frequent case in production. Considering this fact, several attempts with unsupervised and semi-supervised learning models, e.g., GMM (Gaussian mixture model) [147], SSAE (stacked sparse autoencoder) [148,149], and GAN (generative adversarial network) [150], were investigated in order to reduce the reliance on historical failure data in terms of prognostics and health management (PHM). Regarding the cost factor, Palau et al [152] provided a methodology to assess the optimal multi-agent system (MAS) architecture for collaborative PdM in large fleets of industrial assets by using a distributed k-means clustering algorithm (ES-factor).…”

Section: Predictive Maintenancementioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

189

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Section: Predictive Maintenancementioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

189

View full text Add to dashboard Cite

show abstract

“…Digital twins provide new perspectives for process monitoring and prediction of UPM and enabling the influence evaluation of working conditions on the tool and the decision support of the process adjustment. Liu et al [44] proposed a digital twin-based machining process evaluation method to improve product quality under the dynamic changing of the machining status. Zhu et al [28] presented a digital twin-based thin-walled part manufacturing solution to deal with the workpiece changes and to make the set-up activities more rapid and accurate.…”

Section: Digital Twins-based Process Monitoringmentioning

confidence: 99%

“…For instance, Tong et al [60] used the MTConnect protocol to develop a real-time machining digital twin data service for optimizing manufacture, e.g., machining dynamics, contour error estimation, and parameters compensation. Ladj et al [44] proposed a new blueprint of digital shadow to support decision management and data analytics. Knowledge-based behavior models are persistently enriched by unsupervised learning and knowledge inference engine to improve the contextual digital shadow of the physical equipment throughout its operational stages.…”

Section: Data Analytics For Sustainable and Self-optimizing Upmmentioning

confidence: 99%

Digital Twins-Based Smart Design and Control of Ultra-Precision Machining: A Review

Leng

2021

Symmetry

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Ultra-Precision Machining (UPM) is a kind of highly accurate processing technology developed to satisfy the manufacturing requirements of high-end cutting-edge products including nuclear energy producers, very large-scale integrated circuits, lasers, and aircraft. The information asymmetry phenomenon widely exists in the design and control of ultra-precision machining. It may lead to inconsistency between the designed performance and operational performance of the UPM equipment on stiffness, thermal stability, and motion accuracy, which result from its design, manufacturing, and control, and determine the form accuracy and surface roughness of machined parts. The performance of the UPM equipment should be improved continuously. It is still challenging to realize the real-time and self-adaptive control, in which building a high-fidelity and computationally efficient digital twin is a valuable solution. Nevertheless, the incorporation of the digital twin technology into the UPM design and control remains vague and sometimes contradictory. Based on a literature search in the Google Scholar database, the critical issues in the UPM design and control, and how to use the digital twin technologies to promote it, are reviewed. Firstly, the digital twins-based UPM design, including bearings module design, spindle-drive module design, stage system module design, servo module design, and clamping module design, are reviewed. Secondly, the digital twins-based UPM control studies, including voxel modeling, process planning, process monitoring, vibration control, and quality prediction, are reviewed. The key enabling technologies and research directions of digital twins-based design and control are discussed to deal with the information asymmetry phenomenon in UPM.

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“…They can enable numerous applications to use these data to perform a comprehensive analysis on the corresponding machines or the manufacturing systems. To allow incident detection and the deciphering of the related operation context of the machining incident, a DS is proposed in [23]. The framework proposed for knowledge-based DSs allows the integration of data from diverse sources to create the DS, including from the machine tool itself and the smart sensing devices.…”

Section: Dts and Dt-based Industry 40 Application Developmentmentioning

confidence: 99%

Digital Twins Supporting Efficient Digital Industrial Transformation

Bamunuarachchi

Georgakopoulos

Banerjee

et al. 2021

Sensors

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Industry 4.0 applications help digital industrial transformation to be achieved through smart, data-driven solutions that improve production efficiency, product consistency, preventive maintenance, and the logistics of industrial applications and related supply chains. To enable and accelerate digital industrial transformation, it is vital to support cost-efficient Industry 4.0 application development. However, the development of such Industry 4.0 applications is currently expensive due to the limitations of existing IoT platforms in representing complex industrial machines, the support of only production line-based application testing, and the lack of cost models for application cost/benefit analysis. In this paper, we propose the use of Cyber Twins (CTs), an extension of Digital Twins, to support cost-efficient Industry 4.0 application development. CTs provide semantic descriptions of the machines they represent and incorporate machine simulators that enable application testing without any production line risk and cost. This paper focuses on CT-based Industry 4.0 application development and the related cost models. Via a case study of a CT-based Industry 4.0 application from the dairy industry, the paper shows that CT-based Industry 4.0 applications can be developed with approximately 60% of the cost of IoT platform-based application development.

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A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective

Cited by 116 publications

References 41 publications

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Digital Twins-Based Smart Design and Control of Ultra-Precision Machining: A Review

Digital Twins Supporting Efficient Digital Industrial Transformation

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