Deep learning-enabled intelligent process planning for digital twin manufacturing cell

Zhang, Chao; Zhou, Guanghui; Jun-sheng, HU; Li, Jing

doi:10.1016/j.knosys.2019.105247

Cited by 68 publications

(21 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the quality control stage, classical supervised ML models, such as ANN, decision tree, and SVM, were expected to detect or predict potential deformations and surface deviations in production [ 101 , 102 ]. Deep learning (DL) computer vision models, including residual and convolutional neural networks were deployed to recognize eventual quality issues during the automatic production and machining features of parts [ 103 , 104 ], which could be further utilized to enhance the quality and efficiency of assembly processes [ 108 ] ( E -factor), or retraced to the production planning stage in order to support decision making on the basis of historical production knowledge [ 109 ], as a “smart expert” in a collaborative environment ( SG -factor). Following the general concept of integrating ML methods into the digital production twins [ 110 ], DTs of production systems in combination with MBSE can be modeled and adapted modularly as a virtual testbed, which in turn could provide a runtime environment for simulation-based optimization [ 111 , 112 ].…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

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

Huang

Shen²,

et al. 2021

Sensors

121

View full text Add to dashboard Cite

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.

show abstract

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

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

Huang

Shen²,

et al. 2021

Sensors

121

View full text Add to dashboard Cite

show abstract

“…After implementing the digital twin approach, the diameter error of the micro-dots decreased from 2.226 µm to 0.396 µm. Zhang et al [39] proposed a deep learning-enabled smart process planning approach towards a digital twin manufacturing cell (Figure 8). They defined the digital twin manufacturing cell (DTMC) as an integrated multi-physics, multiscale, probabilistic simulation model of a manufacturing cell which can intelligently perceive, simulate, understand, predict, optimize, and control to maximize the part quality and throughput, while maintaining flexibility and reducing cost.…”

Section: Digital Twins-based Process Planningmentioning

confidence: 99%

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

Leng

2021

Symmetry

View full text Add to dashboard Cite

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.

show abstract

“…The feedback between digital models located at the cyber space level and real objects positioned at the physical level is implemented to verify the type, quality and associated functionality of the data obtained in simulation. The data about the actual work of the equipment can be used in the analysis of emergencies for predictive analytics and machine learning [37]. Moreover, the tracking of physical objects can enable control of the key parameters of both individual equipment and the entire production system work [1].…”

Section: (C) Monitoring Data Collection and Data/information Infrastructurementioning

confidence: 99%

Development of an industrial cyber-physical platform for small series production using digital twins

Yablochnikov

Chukichev

Timofeeva

et al. 2021

Phil. Trans. R. Soc. A.

View full text Add to dashboard Cite

The article describes an industrial cyber-physical platform for small series production using digital twins under development at ITMO University (Saint Petersburg, Russia). The platform is based on the following approaches: group technology, adaptive and selective assembling, and digital twin of production systems and processes. The article presents a mechanism for constructing a unified manufacturing process, and results of an integrated multiscale simulation of an injection moulding process. The issues of ensuring identification and monitoring of objects of the industrial cyber-physical platform are considered. Specific service applications required to implement the smart product concept are discussed. The combination of the considered technologies is used to create digital twins of production system objects. All humans that have different roles in the product value stream can interact with the industrial cyber-physical platform at the three levels, receiving support in performing their tasks. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

show abstract

Deep learning-enabled intelligent process planning for digital twin manufacturing cell

Cited by 68 publications

References 37 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

Development of an industrial cyber-physical platform for small series production using digital twins

Contact Info

Product

Resources

About