Application of Machine Learning in Industry 4.0
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 1763
Special Issue Editors
Interests: heuristics and metaheuristics; lean management; machine and deep learning; production planning and control; process modeling and optimization; simulation modeling
Special Issues, Collections and Topics in MDPI journals
Interests: lean production; industrial logistics; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Industry 4.0 is reshaping the future of manufacturing and industrial processes through a convergence of cutting-edge technologies, including collaborative robotics (Cobots), the Internet of Things (IoT), cyber-physical systems (CPS), big data analytics, artificial intelligence (AI), augmented reality (AR), virtual reality (VR), additive manufacturing (3D printing), blockchain, edge computing, autonomous vehicles, and 5G connectivity, among others.
As data are becoming increasingly central to modern industrial operations, machine and deep learning algorithms should play a pivotal role by providing invaluable tools for extracting meaningful insights and optimizing complex processes. In this sense, they are poised to revolutionize Industry 4.0 by driving automation, efficiency, and innovation across various sectors. Specifically, the continued advancement and implementation of machine learning in Industry 4.0 should not only enhance operational efficiency, but also pave the way for novel business models and transformative industrial practices, solidifying its pivotal role in shaping the future of manufacturing and production.
Starting from these premises, in this Special Issue, we aim to explore a fundamental research question: "How can machine learning and deep learning empower and optimize the capabilities of these Industry 4.0 factors?". We welcome research papers that provide practical insights and efficient solutions to harness the potential of these advanced technologies for enhanced decision-making, automation, and productivity in industrial settings. Furthermore, while Industry 4.0 lays the foundation for automation and data-driven processes, we also encourage contributions that touch upon the aspect of "human-centricity". This is a nod to the evolving concept of Industry 5.0, emphasizing the importance of human skills, creativity, and collaboration amid technological advancements.
We look forward to receiving your contributions that explore the intersections of machine learning, Industry 4.0, and the evolving landscape of Industry 5.0. Conceptual models, practical implementation, and use cases are all welcome.
A non-exhaustive list of possible topics is as follows:
- Analysis of large datasets, facilitating predictive maintenance, quality control, and streamlined production;
- Process and warehouse automation, automated guided vehicles, and decentralized fleet management;
- Integration of discrete event simulation and machine/deep learning techniques, for process design, control, and optimization;
- Development of intelligent systems capable of autonomous decision-making, leading to heightened productivity and cost-effectiveness;
- Amalgamation of machine learning with emerging technologies like the IoT and cloud computing to augment the capacity for real-time data analysis and adaptive manufacturing processes;
- Creation of agile, responsive industrial ecosystems capable of swiftly adapting to dynamic market demands.
Dr. Francesco Zammori
Dr. Davide Mezzogori
Guest Editors
Manuscript Submission Information
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Keywords
- Industry 4.0, machine learning
- deep learning
- reinforcement learning
- big data analysis
- discrete event simulation
- agent-based simulation
- process automation
- warehouse automation
- fleet management
- agile systems
- IoT
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