Industrial Informatics and Digital Twin

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 7339

Special Issue Editors

Department of Mechanical Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada
Interests: robotics and mechatronics; high-performance parallel robotic machine development; sustainable/green manufacturing systems; micro/nanomanipulation and MEMS devices (sensors); micro mobile robots and control of multi-robot cooperation; intelligent servo control system for the MEMS-based high-performance micro-robot; web-based remote manipulation; rehabilitation robot and rescue robot
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Special Issue Information

Dear Colleagues,

The essence of the digital twin is virtual–real fusion, which uses sensors, computational models, and industrial data to provide predictions for the current and future states of physical systems. The expected benefit of adopting this high-fidelity approach is to ultimately reduce the uncertainty in the performance of the physical automated system in use.

To capture and comprehend the full complexity of automated systems, the fusion of multi-physics, multi-scale, multi-stage industrial data is required in the digital twin, and frequent and regular (even real-time) updates of the previous predictions through the acquired data are also essential. However, huge challenges still lie in handling the high data variety, complexity, and timeliness embedded in the controlling and decision-making in automated systems.

Industrial information technology, such as AR/VR, cloud computing, deep learning, and knowledge graph, is changing dramatically and has shown promising prospects for managing massive industrial data, establishing digital twin models, and enabling smart services for automated systems. To this end, this Special Issue solicits articles relating to the automation area for digital twins, and concentrates on digital twin models, digital twin informatics, and digital twin behavior in automatic systems to develop research on self-decision making, self-adaptation, and automatic evolution of automatic systems. Topics of interest include but are not limited to the following areas:

  1. Methodologies for digital twins of automation system

System architectures for digital twins;

Representation and modelling for digital twins.

  1. Industrial informatics for digital twins

Industrial knowledge graph of digital twins;

Graph neural networks for industrial knowledge graph;

Verification techniques for digital twins.

  1. Digital Twin-Driven Approaches for automation system

Integrating digital twins with existing industrial approaches such as Industry 4.0;

Augmented reality and virtual reality;

Informatics-based and digital twin-enabled industrial services.

Prof. Dr. Dan Zhang
Prof. Dr. Jinsong Bao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

13 pages, 4103 KiB  
Article
Vision-Aided Brush Alignment Assembly System for Precision Conductive Slip Rings
by Xiaobo Chen, Yukun Wang, Ying Sheng, Chengyi Yu, Xiao Yang and Juntong Xi
Machines 2022, 10(5), 393; https://doi.org/10.3390/machines10050393 - 19 May 2022
Cited by 3 | Viewed by 1775
Abstract
The alignment precision of manual brush assembly for a precision conductive slip ring is critical to its performance of reliability and service lifetime. Currently, the alignment precision cannot be guaranteed since it largely depends on the operator’s experiences and skill level. In this [...] Read more.
The alignment precision of manual brush assembly for a precision conductive slip ring is critical to its performance of reliability and service lifetime. Currently, the alignment precision cannot be guaranteed since it largely depends on the operator’s experiences and skill level. In this paper, a machine vision-aided method is proposed to measure the ring groove positions as the brush alignment objective, and track the relative brush position deviation during the manual brush alignment assembly. A vision-aided brush alignment assembly system is also developed to provide quantitative position deviation for the precise alignment of the brush and the ring groove, ensuring higher alignment accuracy and efficiency. The experimental results indicate that, with the developed system, the brush alignment assembly accuracy can be controlled within ±0.02 mm. Full article
(This article belongs to the Special Issue Industrial Informatics and Digital Twin)
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21 pages, 4065 KiB  
Article
A Performance Prediction Method for a High-Precision Servo Valve Supported by Digital Twin Assembly-Commissioning
by Xuemin Sun, Shimin Liu, Jinsong Bao, Jie Li and Zengkun Liu
Machines 2022, 10(1), 11; https://doi.org/10.3390/machines10010011 - 23 Dec 2021
Cited by 12 | Viewed by 2737
Abstract
The manufacturing of a high-precision servo valve belongs to multi-variety, small-batch, and customized production modes. In the process of assembly and commissioning, various characteristic parameters are critical indicators to measure product performance. To meet the performance requirements of a high-precision servo valve, the [...] Read more.
The manufacturing of a high-precision servo valve belongs to multi-variety, small-batch, and customized production modes. In the process of assembly and commissioning, various characteristic parameters are critical indicators to measure product performance. To meet the performance requirements of a high-precision servo valve, the traditional method usually relies on the test bench and manual experience for continuous trial and error commissioning, which significantly prolongs the whole assembly-commissioning cycle. Therefore, this paper proposed a performance prediction method for a high-precision servo valve supported by digital twin assembly-commissioning. Firstly, the cloud-edge computing network is deployed in the digital twin assembly-commissioning system to improve the efficiency and flexibility of data processing. Secondly, the method workflow of performance prediction is described. In order to improve the accuracy of measurement data, a data correction method based on model simulation and gross error processing is proposed. Aiming at the problem of high input dimension of the prediction model, a key assembly feature parameters (KAFPs) selection method, based on information entropy (IE), is proposed and given interpretability. Additionally, to avoid the poor prediction accuracy caused by small sample data, a performance prediction method based on TrAdaboost was utilized. Finally, the hysteresis characteristic commissioning of a high-precision servo valve is taken as an example to verify the application. The results indicate that the proposed method would enable accurate performance prediction and fast iteration of commissioning decisions. Full article
(This article belongs to the Special Issue Industrial Informatics and Digital Twin)
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13 pages, 3161 KiB  
Article
Infrared Laser Speckle Projection-Based Multi-Sensor Collaborative Human Body Automatic Scanning System
by Xiao Yang, Juntong Xi, Jingyu Liu and Xiaobo Chen
Machines 2021, 9(11), 299; https://doi.org/10.3390/machines9110299 - 22 Nov 2021
Viewed by 1976
Abstract
Human body scanning is an important means to build a digital 3D model of the human body, which is the basis for intelligent clothing production, human obesity analysis, and medical plastic surgery applications, etc. Comparing to commonly used optical scanning technologies such as [...] Read more.
Human body scanning is an important means to build a digital 3D model of the human body, which is the basis for intelligent clothing production, human obesity analysis, and medical plastic surgery applications, etc. Comparing to commonly used optical scanning technologies such as laser scanning and fringe structured light, infrared laser speckle projection-based 3D scanning technology has the advantages of single-shot, simple control, and avoiding light stimulation to human eyes. In this paper, a multi-sensor collaborative digital human body scanning system based on near-infrared laser speckle projection is proposed, which occupies less than 2 m2 and has a scanning period of about 60 s. Additionally, the system calibration method and control scheme are proposed for the scanning system, and the serial-parallel computing strategy is developed based on the unified computing equipment architecture (CUDA), so as to realize the rapid calculation and automatic registration of local point cloud data. Finally, the effectiveness and time efficiency of the system are evaluated through anthropometric experiments. Full article
(This article belongs to the Special Issue Industrial Informatics and Digital Twin)
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