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Actuators
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AI, Designing, Sensing, Instrumentation, Diagnosis, Controlling, and Integration of Actuators...
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AI, Designing, Sensing, Instrumentation, Diagnosis, Controlling, and Integration of Actuators in Digital Manufacturing—Volume II

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Manufacturing Systems".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1229

Special Issue Editors

Prof. Dr. Zhuming Bi

E-Mail Website
Guest Editor
Department of Civil and Mechanical Engineering, Purdue University Fort Wayne, Fort Wayne, IN, USA
Interests: enterprise information systems; digital manufacturing; finite element analysis; machine designs; robotics and automation; enterprise systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aki Mikkola

E-Mail Website
Guest Editor
Laboratory of Machine Design, Department of Mechanical Engineering, LUT-University, 53851 Lappeenranta, Finland
Interests: multibody system dynamics; computational dynamics; flexible multibody dynamics; real-time simulation
Prof. Dr. Guilin Yang

E-Mail Website
Guest Editor
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
Interests: precision motion system; advanced robotics; intelligent manufacturing technology; modelling and simulation; system integration; instrumentation; engineering management
Dr. Yuk-Ming Tang

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong
Interests: virtual reality; artificial intelligence; digital twin; Industry 4.0; sustainable technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Leung Yung

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong, China
Interests: precision engineering; product mechatronics; automatic control system; computer integrated manufacturing and management; computer vision; 3D model retrieval; logistic planning and optimization; deep space exploration
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrew W. H. Ip

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada
Interests: radio frequency identification (RFID) and supply chain technologies; wireless sensor networks (WSNs); decision support systems and artificial intelligence (AI); big data analytics; resilience engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Actuators are usually essential enablers to implement the functions of a device, product, or system. As primary system elements, actuators are required to be integrated with other system elements such as other actuators, sensors, end-effectors, and embedded controls to fulfill their functions. Therefore, modern actuators have advanced greatly through the incorporation of newly developed digital technologies such as artificial intelligence (AI), cyber–physical systems (CPSs), Internet of Things (IoT), digital twins (DT-I), cloud computing (CC), digital triads (DT-II), additive manufacturing, predictive manufacturing, blockchain technologies (BCT), and big data analytics (BDA). This Special Issue aims to collect some representative studies on the development of new machines, products, and systems in digital manufacturing, especially in aerospace engineering, with merits in the designing, sensing, instrumentation, diagnosis, control, and integration of actuators. The topics that will constitute this Special Issue include, but are not limited to, the following:

  • Literature surveys on working principles, methodologies, and applications of actuators;
  • AI, openAI, and chatGPT in engineering;
  • Smart actuators;
  • Macro/micro systems;
  • Temperature-compensation actuators;
  • Mechatronic designs of actuators;
  • New fabrication, assembly, and testing of actuators;
  • Actuators from additive manufacturing;
  • Predictive controls;
  • Improvement of existing actuators;
  • Actuators in defense and space explorations and other applications;
  • Actuators in productions;
  • Actuators in digital manufacturing;
  • Actuators in cyber–physical systems;
  • Actuators in robotics;
  • New technologies such as sensing, diagnosis, modelling, and analysis relevant to actuators;
  • Self-powered actuators, sensors, and cyber–physical sensors;
  • Modelling methods for analyses and optimization of actuators;
  • Actuators in digital manufacturing;
  • Designs of actuators in special applications, especially in deep sea or space explorations;
  • Actuators in mechatronic products and systems.

Prof. Dr. Zhuming Bi
Prof. Dr. Aki Mikkola
Prof. Dr. Guilin Yang
Dr. Yuk-Ming Tang
Prof. Dr. Kai Leung Yung
Prof. Dr. Andrew W. H. Ip
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. Actuators 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 (2 papers)

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Research

15 pages, 1957 KiB  
Article
Traffic Signal Control Optimization Based on Neural Network in the Framework of Model Predictive Control
by Dapeng Tang and Yuzhou Duan
Actuators 2024, 13(7), 251; https://doi.org/10.3390/act13070251 - 1 Jul 2024
Viewed by 422
Abstract
To improve the effectiveness of model predictive control (MPC) in dynamic traffic signal control strategies, it has been combined with graph convolutional networks (GCNs) and deep reinforcement learning (DRL) technologies. In this study, a neural-network-based traffic signal control optimization method under the MPC [...] Read more.
To improve the effectiveness of model predictive control (MPC) in dynamic traffic signal control strategies, it has been combined with graph convolutional networks (GCNs) and deep reinforcement learning (DRL) technologies. In this study, a neural-network-based traffic signal control optimization method under the MPC framework is proposed. A dynamic correlation matrix is introduced in the predictive model to adapt to the dynamic changes in correlations between nodes over time. The signal control optimization strategy is solved using DRL, where the agent explores the optimal control strategy based on pre-set constraints in the future road environment. The geometric structure and traffic flow data of a real intersection were selected as the simulation validation environment, and a joint simulation was conducted using Python and SUMO. The experimental results indicate that in low-traffic scenarios, the queue length is reduced by more than 2 vehicles compared to the selected comparison methods; in high-traffic scenarios, the queue length is reduced by an average of 17 vehicles. Under the actual traffic data of the intersection, the average speed is increased by 6.4% compared to the fixed timing method; compared to the inductive signal control method, it increases from 9.76 m/s to 11.69 m/s, an improvement of 19.7%, effectively enhancing the intersection signal control performance. Full article
(This article belongs to the Special Issue AI, Designing, Sensing, Instrumentation, Diagnosis, Controlling, and Integration of Actuators in Digital Manufacturing—Volume II)
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17 pages, 5822 KiB  
Article
Fault Detection of Flow Control Valves Using Online LightGBM and STL Decomposition
by Shaodong Liu, Tao Zhao and Dengfeng Zhang
Actuators 2024, 13(6), 222; https://doi.org/10.3390/act13060222 - 13 Jun 2024
Viewed by 510
Abstract
In the process industrial systems, flow control valves are deemed vital components that ensure the system’s safe operation. Hence, detecting faults in control valves is of significant importance. However, the stable operating conditions of flow control valves are prone to change, resulting in [...] Read more.
In the process industrial systems, flow control valves are deemed vital components that ensure the system’s safe operation. Hence, detecting faults in control valves is of significant importance. However, the stable operating conditions of flow control valves are prone to change, resulting in a decreased effectiveness of the conventional fault detection method. In this paper, an online fault detection approach considering the variable operating conditions of flow control valves is proposed. This approach is based on residual analysis, combining LightGBM online model with Seasonal and Trend decomposition using Loess (STL). LightGBM is a tree-based machine learning algorithm. In the proposed method, an online LightGBM is employed to establish and continuously update a flow prediction model for control valves, ensuring model accuracy during changes in operational conditions. Subsequently, STL decomposition is applied to the model’s residuals to capture the trend of residual changes, which is then transformed into a Health Index (HI) for evaluating the health level of the flow control valves. Finally, fault occurrences are detected based on the magnitude of the HI. We validate this approach using both simulated and real factory data. The experimental results demonstrate that the proposed method can promptly reflect the occurrence of faults through the HI. Full article
(This article belongs to the Special Issue AI, Designing, Sensing, Instrumentation, Diagnosis, Controlling, and Integration of Actuators in Digital Manufacturing—Volume II)
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