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Industry 4.0 and Industry 5.0: Simulators and Algorithms in Manufacturing...
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Industry 4.0 and Industry 5.0: Simulators and Algorithms in Manufacturing Processes and Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2555

Special Issue Editors

Dr. Wei Guo

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
Interests: machine learning; intelligent manufacturing; social manufacturing; federated learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiewu Leng

E-Mail Website
Guest Editor
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China
Interests: intelligent product design; intelligent product manufacturing; multi-objective optimization; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Dolleagues,

The rapid evolution of digital technologies has ushered in a new era for manufacturing and industrial systems, often referred to as Industry 4.0. This revolution is characterized by the integration of cyber-physical systems (CPS), Internet of Things (IoT), artificial intelligence (AI), big data, and advanced robotics, enabling the creation of smart factories. Industry 4.0 emphasizes automation, real-time data exchange, and decentralized decision-making to optimize manufacturing processes and supply chain operations. As the industrial landscape continues to evolve, the emergence of Industry 5.0 brings a renewed focus on the collaboration between humans and machines. Industry 5.0 emphasizes human-centric solutions that integrate automation with human intelligence, creativity, and decision-making capabilities. It also places a strong emphasis on sustainability, personalization, and the societal impact of technological advancements.

This special issue on “Industry 4.0 and Industry 5.0: Simulators and Algorithms” seeks high-quality works focusing on exploring the key role of simulators and algorithms in the transition from Industry 4.0 to Industry 5.0. Simulators serve as powerful tools for modeling, optimizing, and testing industrial processes in a virtual environment, enabling industries to reduce risks, improve efficiency, and facilitate innovation. Meanwhile, algorithms, particularly those leveraging AI and machine learning, are essential for optimizing resource allocation, enhancing predictive maintenance, and supporting human-machine collaboration.

Topics include, but are not limited to:

  • Development and application of simulators for smart manufacturing systems
  • Algorithms for predictive maintenance, resource optimization, and decision-making in Industry 4.0 and 5.0
  • Human-centric AI algorithms in Industry 5.0
  • Integration of IoT, CPS, and AI for real-time monitoring and control
  • Sustainable manufacturing algorithms and energy-efficient solutions
  • Case studies demonstrating the transition from Industry 4.0 to Industry 5.0
  • Federated learning applications in Industry 4.0 and 5.0
  • Digital twins for real-time industrial process monitoring and control;
  • Robotics and human–robot collaboration algorithms in Industry 5.0;

Dr. Wei Guo
Prof. Dr. Jiewu Leng
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. Processes 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.

Keywords

  • Industry 4.0
  • Industry 5.0
  • smart manufacturing
  • digital twin
  • human–machine collaboration
  • industrial simulators
  • AI algorithms
  • cyber-physical systems (CPS)
  • predictive maintenance
  • federated learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

24 pages, 6641 KiB  
Article
Optimal Dispatching Rules for Peak Shaving of Cascaded Hydropower Stations in Response to Large-Scale New Energy Integration
by Zhanxing Xu, Qiang Liu, Lingjun Xu, Li Mo, Yongchuan Zhang and Xin Zhang
Processes 2025, 13(3), 612; https://doi.org/10.3390/pr13030612 - 21 Feb 2025
Viewed by 341
Abstract
Fully tapping into the load regulation capacity of cascade hydropower stations on a river, in coordination with wind and photovoltaic power stations, can effectively suppress power fluctuations in new energy and promote grid integration and the consumption of new energy. To derive the [...] Read more.
Fully tapping into the load regulation capacity of cascade hydropower stations on a river, in coordination with wind and photovoltaic power stations, can effectively suppress power fluctuations in new energy and promote grid integration and the consumption of new energy. To derive the peak shaving dispatching rules for cascaded hydropower stations in provincial power systems with a high proportion of new energy integration, a short-term peak shaving dispatching model for cascaded hydropower stations was first established considering large-scale new energy consumption; secondly, based on statistical learning methods, the peak shaving and dispatching rules of cascade hydropower stations in response to large-scale new energy integration were derived. Finally, taking wind farms, photovoltaic power stations, and the Qingjiang cascade hydropower stations in the power grid of Hubei Province, China, as research objects, the compensation effect of Qingjiang cascade hydropower stations on new energy output and the peak shaving performance for the power grid load were verified. The research results indicate that cascade hydropower can effectively reduce the peak valley load difference in provincial power grids and improve the overall smoothness of power grid loads while suppressing fluctuations in new energy output. After peak regulation by cascade hydropower, the residual load fluctuation indices of the power grid are improved by more than 20% compared to those after the integration of new energy. The probabilistic dispatching decisions for the facing period’s output through the optimal dispatching rules of cascade hydropower stations can provide dispatchers with richer decision-making support information and have guiding significance for the actual peak shaving dispatch of cascade hydropower stations. Full article
(This article belongs to the Special Issue Industry 4.0 and Industry 5.0: Simulators and Algorithms in Manufacturing Processes and Systems)
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22 pages, 4497 KiB  
Article
Optimal Peak-Shaving Dispatching of Hydropower Station in Response to Long-Term Load Demand of Power Grid
by Zhanxing Xu, Chang Liu, Qiang Liu, Lingjun Xu, Li Mo and Yongchuan Zhang
Processes 2025, 13(2), 489; https://doi.org/10.3390/pr13020489 - 10 Feb 2025
Viewed by 638
Abstract
Hydropower stations play a crucial role in meeting the demand for peak shaving in the power grid. A method called the adaptive segmented cutting load algorithm (ASCLA) is proposed to address the problem of the uneven distribution of regulation effects when formulating long-term [...] Read more.
Hydropower stations play a crucial role in meeting the demand for peak shaving in the power grid. A method called the adaptive segmented cutting load algorithm (ASCLA) is proposed to address the problem of the uneven distribution of regulation effects when formulating long-term peak-shaving dispatching plans for hydropower stations. This method mainly consists of three components: full-period load segmentation, sub-period end water level treatment, and staged cutting load optimization. It can improve the average regulation ability of hydropower stations for the power grid’s long-term load by combining the inflow conditions. In order to compare the progressiveness of the proposed method, the Three Gorges hydropower station and the Central China Power Grid were used as research objects, and its long-term peak-shaving performance was analyzed by comparing it with that of the classical HCL solution method. The simulation dispatching results show that the proposed method resulted in significantly improved peak-shaving indicators, such as the mean squared deviation of the rolling window, load fluctuation index, and peak value compared to HCL. In years with abundant reservoir runoff, the comprehensive improvement can reach about 25%, indicating that the proposed ASCLA has more advantages in responding to the long-term load regulation needs of the power grid compared to existing methods. The research results of this paper can provide a reference and guidance for peak-shaving dispatching in hydropower stations during the dry season, effectively improving the long-term peak-shaving benefits of hydropower stations. Full article
(This article belongs to the Special Issue Industry 4.0 and Industry 5.0: Simulators and Algorithms in Manufacturing Processes and Systems)
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21 pages, 4429 KiB  
Article
Deep Reinforcement Learning-Based Robotic Puncturing Path Planning of Flexible Needle
by Jun Lin, Zhiqiang Huang, Tengliang Zhu, Jiewu Leng and Kai Huang
Processes 2024, 12(12), 2852; https://doi.org/10.3390/pr12122852 - 12 Dec 2024
Viewed by 881
Abstract
The path planning of flexible needles in robotic puncturing presents challenges such as limited model accuracy and poor real-time performance, which affect both efficiency and accuracy in complex medical scenarios. To address these issues, this paper proposes a deep reinforcement learning-based path planning [...] Read more.
The path planning of flexible needles in robotic puncturing presents challenges such as limited model accuracy and poor real-time performance, which affect both efficiency and accuracy in complex medical scenarios. To address these issues, this paper proposes a deep reinforcement learning-based path planning method for flexible needles in robotic puncturing. Firstly, we introduce a unicycle model to describe needle motion and design a hierarchical model to simulate layered tissue interactions with the needle. The forces exerted by tissues at different positions on the flexible needle are considered, achieving a combination of kinematic and mechanical models. Secondly, a deep reinforcement learning framework is built, integrating obstacle avoidance and target attraction to optimize path planning. The design of state features, the action space, and the reward function is tailored to enhance the model’s decision-making capabilities. Moreover, we incorporate a retraction mechanism to bolster the system’s adaptability and robustness in the dynamic context of surgical procedures. Finally, laparotomy simulation results validate the proposed method’s effectiveness and generalizability, demonstrating its superiority over current state-of-the-art techniques in robotic puncturing. Full article
(This article belongs to the Special Issue Industry 4.0 and Industry 5.0: Simulators and Algorithms in Manufacturing Processes and Systems)
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