AI and IoT Convergence for Sustainable Smart Manufacturing

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Actuators, Sensors and Devices".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 4510

Special Issue Editors


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Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Interests: metaheuristic algorithms; machine learning; internet of things; wireless networks; computational management science
Special Issues, Collections and Topics in MDPI journals
Faculty of Transdisciplinary Innovation, University of Technology Sydney, Ultimo 2007, Australia
Interests: data science; network analysis and visualisation; human–computer interactions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As customers increasingly pay attention to sustainability, the global high-tech and manufacturing industries are required to develop green growth strategies to achieve net-zero emissions from various perspectives. To ensure long-term competitiveness, smart factories must manufacture products that meet the environmental, social, and governance (ESG) standards. Government regulations on energy saving, carbon reduction, carbon trading, carbon neutrality, and carbon tax are also becoming increasingly stringent. Noncompliant products will be subject to additional charges, and companies will need to trade carbon credits with other companies to satisfy the standards. These requirements increase the production cost, and further affect the company’s competitiveness in the global supply chain. Therefore, considering net-zero carbon emissions in smart manufacturing (a.k.a, sustainable smart manufacturing) will play a key role in global competitiveness.

In sustainable smart manufacturing, it is required to meet the ESG standards to minimize resource and energy consumption and reduce or eliminate carbon emissions in the manufacturing process to reach net-zero carbon emissions. The operations of sustainable smart manufacturing are highly reliant on sustainable and mutually supportive systems assisted by the Internet of things (IoT), artificial intelligence (AI), smart manufacturing devices, systems and services such as sensors and actuators, and other smart technologies. In sustainable smart factories, the cloud center continuously collects real-time information on production, energy consumption, pollution, and emissions from manufacturing sites through sensors and the IoT, then adopts AI and deep learning algorithms to identify objects and sense the environment, and finally, implements smart physical operations through actuators, which enable them to react in real time to carbon emissions, anomalies, emergencies, and so on. Furthermore, additional functions, such as prediction and simulation, are derived to create ESG-compliant products, zero-carbon production lines, sustainable smart factories, and other valuable applications.

Therefore, this Special Issue encourages new thinking and discussion on how AI and IoT technologies can address the many key issues of energy management, optimization, and the performance of sensors and actuators in sustainable smart manufacturing.

The topics of interest include, but are not limited to, the following:

  • AI and IoT applications for sustainable smart manufacturing;
  • AI and IoT technologies for the energy management of sensors and actuators;
  • AI and IoT technologies for energy storage systems of sensors and actuators;
  • AI and IoT technologies for wireless energy harvesting;
  • AI and IoT technologies for the deployment and scheduling of sensors and actuators;
  • AI and IoT technologies for obtaining ESG data and analysis;
  • Design of sensors and actuators in sustainable smart manufacturing;
  • Industry 4.0 for sustainable smart manufacturing;
  • Energy distribution of sensors and actuators;
  • Green energy and carbon footprint of sensors and actuators;
  • Sustainability and net-zero carbon emissions in smart manufacturing processes;
  • ESG applications in smart manufacturing;
  • Novel applications of sensors and actuators in sustainable smart manufacturing.

Prof. Dr. Chun-Cheng Lin
Dr. Tony Huang
Guest Editors

Manuscript Submission Information

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Keywords

  • sustainable smart manufacturing
  • environmental, social, and governance (ESG) standards
  • industry 4.0
  • artificial intelligence (AI)
  • internet of things (IoT)
  • sensors and actuators
  • energy distribution and management
  • mathemtical and computational modelling
  • machine/deep learning
  • deployment and scheduling
  • wireless energy harvesting

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Published Papers (2 papers)

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26 pages, 3637 KiB  
Article
Pathway to Smart Maintenance: Integrating Engineering and Economics Modeling
by Rakshith Badarinath, Kai-Wen Tien and Vittaldas V. Prabhu
J. Sens. Actuator Netw. 2025, 14(1), 16; https://doi.org/10.3390/jsan14010016 - 4 Feb 2025
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Abstract
This paper proposes a pathway for smart maintenance by addressing overarching questions and key impediments that arise when manufacturing companies are exploring investments in such projects. The proposed pathway consists of seven distinct steps at which analytical models are used to predict the [...] Read more.
This paper proposes a pathway for smart maintenance by addressing overarching questions and key impediments that arise when manufacturing companies are exploring investments in such projects. The proposed pathway consists of seven distinct steps at which analytical models are used to predict the impact of smart maintenance on system-level operational key performance indicators (KPIs) and the resulting return on investment (ROI). The key advantage of this approach is that the analytical models rely on a few parameters and, therefore, can be used even when there are no sophisticated data collection systems in place, such as in the case of many small and medium enterprises (SMEs). Furthermore, the proposed approach allows for the development of a “personalized” pathway along with the prediction of performance improvement and ROI impact, enabling management to make investment decisions with greater confidence. The proposed pathway also consists of a three-step detour for companies unprepared to embark on their journey towards smart maintenance. The application of the proposed smart maintenance pathway is illustrated through case studies consisting of three real SMEs. First, for companies that are unprepared for smart maintenance, we suggest traditional variance reduction methods and appropriate performance improvement goals along with predicted improvements in operational and financial KPIs. Next, for companies that are prepared to embark on smart maintenance, we provide a detailed evaluation of the impact of condition-based maintenance (CBM) by analyzing various machine combinations that maximize performance-to-cost ratio. In the case of one SME, our analysis shows that an improvement in throughput (0 to 3%) with an ROI (26:1) is achievable through the adoption of smart maintenance, which can be visualized using the DuPont Model. Full article
(This article belongs to the Special Issue AI and IoT Convergence for Sustainable Smart Manufacturing)
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Review

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33 pages, 6102 KiB  
Review
Machine-Learning- and Internet-of-Things-Driven Techniques for Monitoring Tool Wear in Machining Process: A Comprehensive Review
by Sudhan Kasiviswanathan, Sakthivel Gnanasekaran, Mohanraj Thangamuthu and Jegadeeshwaran Rakkiyannan
J. Sens. Actuator Netw. 2024, 13(5), 53; https://doi.org/10.3390/jsan13050053 - 4 Sep 2024
Cited by 7 | Viewed by 3008
Abstract
Tool condition monitoring (TCM) systems have evolved into an essential requirement for contemporary manufacturing sectors of Industry 4.0. These systems employ sensors and diverse monitoring techniques to swiftly identify and diagnose tool wear, defects, and malfunctions of computer numerical control (CNC) machines. Their [...] Read more.
Tool condition monitoring (TCM) systems have evolved into an essential requirement for contemporary manufacturing sectors of Industry 4.0. These systems employ sensors and diverse monitoring techniques to swiftly identify and diagnose tool wear, defects, and malfunctions of computer numerical control (CNC) machines. Their pivotal role lies in augmenting tool lifespan, minimizing machine downtime, and elevating productivity, thereby contributing to industry growth. However, the efficacy of CNC machine TCM hinges upon multiple factors, encompassing system type, data precision, reliability, and adeptness in data analysis. Globally, extensive research is underway to enhance real-time TCM system efficiency. This review focuses on the significance and attributes of proficient real-time TCM systems of CNC turning centers. It underscores TCM’s paramount role in manufacturing and outlines the challenges linked to TCM data processing and analysis. Moreover, the review elucidates various TCM system variants, including cutting force, acoustic emission, vibration, and temperature monitoring systems. Furthermore, the integration of industrial Internet of things (IIoT) and machine learning (ML) into CNC machine TCM systems are also explored. This article concludes by underscoring the ongoing necessity for research and development in TCM technology to empower modern intelligent industries to operate at peak efficiency. Full article
(This article belongs to the Special Issue AI and IoT Convergence for Sustainable Smart Manufacturing)
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