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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—2nd Edition

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 9248

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 46805, USA
Interests: mechanical engineering; robotics; manufacturing engineering; mechatronics
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.

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

18 pages, 3986 KiB  
Article
Modeling and Analysis of Transmission Efficiency for 3K Planetary Gearbox with Flexure-Based Carrier for Backdrivable Robot Joints
by Qinghao Du, Guilin Yang, Weijun Wang, Chin-Yin Chen and Zaojun Fang
Actuators 2025, 14(4), 173; https://doi.org/10.3390/act14040173 - 1 Apr 2025
Viewed by 319
Abstract
A high-gear-ratio anti-backlash 3K planetary gearbox with a preloaded flexure-based carrier is a suitable reducer for robot joints owning to its compact design and high transmission accuracy. However, to design such a 3K planetary gearbox with high bidirectional efficiencies for backdrivable robot joints, [...] Read more.
A high-gear-ratio anti-backlash 3K planetary gearbox with a preloaded flexure-based carrier is a suitable reducer for robot joints owning to its compact design and high transmission accuracy. However, to design such a 3K planetary gearbox with high bidirectional efficiencies for backdrivable robot joints, it is critical to develop an accurate transmission efficiency model to predict the effects of the preloaded flexure-based carrier on the efficiency of the 3K planetary gearbox. To determine the meshing forces of gear pairs in the 3K planetary gearbox, a quasi-static model is formulated according to tangential displacements of planet gears resulting from the preloaded flexure-based carrier. Considering the reverse meshing forces in the anti-backlash 3K planetary gearbox, a modified efficiency model is developed and the bidirectional transmission efficiencies are analyzed. Simulation results show that both forward and backward transmission efficiencies of the anti-backlash 3K planetary gearbox decrease as the preload increases, while they all increase with the increasing load torque. It is also revealed that the preload primarily affects the meshing efficiency of the sun–planet gear pair. Four different carrier prototypes are fabricated for experiments. The average errors between the predicted and measured results for forward and backward transmission efficiencies are 2.30% and 4.01%, respectively. Full article
(This article belongs to the Special Issue AI, Designing, Sensing, Instrumentation, Diagnosis, Controlling, and Integration of Actuators in Digital Manufacturing—2nd Edition)
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26 pages, 1267 KiB  
Article
An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings
by Yongze Jin, Xubo Yang, Junqi Liu, Yanxi Yang, Xinhong Hei and Anqi Shangguan
Actuators 2025, 14(2), 88; https://doi.org/10.3390/act14020088 - 11 Feb 2025
Viewed by 555
Abstract
In this article, a novel prediction index is constructed, a hybrid filtering is proposed, and a remaining useful life (RUL) prediction framework is developed. In the proposed framework, different models are built for different operation states of rolling bearings. In the normal state, [...] Read more.
In this article, a novel prediction index is constructed, a hybrid filtering is proposed, and a remaining useful life (RUL) prediction framework is developed. In the proposed framework, different models are built for different operation states of rolling bearings. In the normal state, a linear model is built, and a Kalman filter (KF) is implemented to determine the failure start time (FST). In the degradation state, a dimensionless prediction index CRRMS is constructed, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold. Then, a double exponential model is established, and the hybrid filtering is proposed to estimate the future trend of CRRMS, which is combined by a particle filter (PF) and an unscented Kalman filter (UKF). At the same time, dynamic failure threshold technology is adaptively used to determine the failure thresholds of different bearings. Furthermore, the RUL is extrapolated at the moment the prediction index exceeds the failure threshold. Finally, the effectiveness and practicability of the proposed method is verified on the bearing dataset given by the PRONOSTIA platform. Full article
(This article belongs to the Special Issue AI, Designing, Sensing, Instrumentation, Diagnosis, Controlling, and Integration of Actuators in Digital Manufacturing—2nd Edition)
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23 pages, 5985 KiB  
Article
A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion
by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du and Zhengqi Wang
Actuators 2025, 14(1), 17; https://doi.org/10.3390/act14010017 - 7 Jan 2025
Viewed by 743
Abstract
Rolling bearings, as critical components of rotating machinery, directly affect the reliability and efficiency of the system. Due to extended operation under high load, harsh environmental conditions, and continuous use, bearings become more susceptible to failure, leading to a higher likelihood of malfunction. [...] Read more.
Rolling bearings, as critical components of rotating machinery, directly affect the reliability and efficiency of the system. Due to extended operation under high load, harsh environmental conditions, and continuous use, bearings become more susceptible to failure, leading to a higher likelihood of malfunction. To prevent sudden failures, reduce downtime, and optimize maintenance strategies, early and accurate diagnosis of rolling bearing faults is essential. Although existing methods have achieved certain success in processing acoustic and vibration signals, they still face challenges such as insufficient feature fusion, inflexible weight allocation, lack of effective feature selection mechanisms, and low computational efficiency. To address these challenges, we propose a dynamic weighted multimodal fault diagnosis model based on the fusion of acoustic and vibration information. This model aims to enhance feature fusion, dynamically adapt to signal characteristics, optimize feature selection, and reduce computational complexity. The model incorporates an adaptive fusion method based on a multi-branch convolutional structure, enabling unified processing of both acoustic and vibration signals. At the same time, a cross-modal dynamic weighted fusion mechanism is employed, allowing the real-time adjustment of weight distribution based on signal characteristics. By utilizing an attention mechanism for dynamic feature selection and weighting, the robustness of classification is further improved. Additionally, when processing acoustic signals, a depthwise separable convolutional network is used, effectively reducing computational complexity. Experimental results demonstrate that our method significantly outperforms other algorithms in terms of convergence speed and final performance. Additionally, the accuracy curve during training showed minimal fluctuation, reflecting higher robustness. The model achieved over 99% diagnostic accuracy under all signal-to-noise ratio (SNR) conditions, showcasing exceptional robustness and noise resistance in both noisy and high-SNR environments. Furthermore, its superiority across different data scales, especially in small-sample learning and stability, highlights its strong generalization capability. Full article
(This article belongs to the Special Issue AI, Designing, Sensing, Instrumentation, Diagnosis, Controlling, and Integration of Actuators in Digital Manufacturing—2nd Edition)
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21 pages, 5979 KiB  
Article
Construction of Knowledge Graph for Air Compressor Fault Diagnosis Based on a Feature-Fusion RoBERTa-BiLSTM-CRF Model
by Xiaqiu Xiao, Buyun Sheng, Gaocai Fu and Yingkang Lu
Actuators 2024, 13(9), 339; https://doi.org/10.3390/act13090339 - 5 Sep 2024
Cited by 3 | Viewed by 1524
Abstract
Diagnosing complex air compressor systems with traditional data-driven deep learning models often results in isolated fault diagnosis, ignoring correlations between concurrent faults. This paper introduces a knowledge graph construction approach for the air compressor fault diagnosis field, using after-sales business data as the [...] Read more.
Diagnosing complex air compressor systems with traditional data-driven deep learning models often results in isolated fault diagnosis, ignoring correlations between concurrent faults. This paper introduces a knowledge graph construction approach for the air compressor fault diagnosis field, using after-sales business data as the source. We propose a model based on Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa), specifically tailored for constructing a knowledge graph for air compressor fault diagnosis. By integrating Whole Word Masking (WWM) technology, Bidirectional Long Short-Term Memory (BiLSTM), and Conditional Random Fields (CRFs), our approach effectively extracts specific entities from unstructured data. On our dataset, the model achieved an average accuracy of 0.7962 and an F1 score of 0.7956, demonstrating notable improvements in both accuracy and recall for entity recognition tasks. The extracted entities were subsequently stored in a Neo4j graph database, facilitating the construction of a domain-specific knowledge graph for air compressor fault diagnosis. Full article
(This article belongs to the Special Issue AI, Designing, Sensing, Instrumentation, Diagnosis, Controlling, and Integration of Actuators in Digital Manufacturing—2nd Edition)
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25 pages, 14913 KiB  
Article
Research on Cloud-Edge-Device Collaborative Intelligent Monitoring System of Grinding Wheel Wear State for High-Speed Cylindrical Grinding of Bearing Rings
by Rongjin Zhuo, Zhaohui Deng, Jimin Ge, Wei Liu, Lishu Lv and Can Yan
Actuators 2024, 13(9), 327; https://doi.org/10.3390/act13090327 - 27 Aug 2024
Viewed by 878
Abstract
Aiming at the problems of grinding wheel wear during high-speed cylindrical grinding, communication delays, and slow response during data acquisition, processing, and system operation, an intelligent online monitoring technology frame for CNC manufacturing units is proposed, incorporating a real-time-perception grinding mechanism and a [...] Read more.
Aiming at the problems of grinding wheel wear during high-speed cylindrical grinding, communication delays, and slow response during data acquisition, processing, and system operation, an intelligent online monitoring technology frame for CNC manufacturing units is proposed, incorporating a real-time-perception grinding mechanism and a cloud-edge device. Based on the grinding data and grinding wheel wear mechanism, a monitoring model using multi-sensor information fusion is constructed to assess the grinding wheel wear state. In addition, edge data acquisition and online monitoring software have been developed to improve the speed of data transmission and processing. Finally, based on the proposed framework, a cloud-edge device collaborative intelligent monitoring system for assessing grinding wheel wear during high-speed cylindrical grinding of bearing rings is constructed. It improves the grinding quality and efficiency, reduces the grinding cost, and incorporates remote control functionality. Full article
(This article belongs to the Special Issue AI, Designing, Sensing, Instrumentation, Diagnosis, Controlling, and Integration of Actuators in Digital Manufacturing—2nd Edition)
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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
Cited by 2 | Viewed by 2950
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—2nd Edition)
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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
Cited by 3 | Viewed by 1270
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—2nd Edition)
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