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Artificial Intelligence and Sensing Technology in Smart Manufacturing
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Artificial Intelligence and Sensing Technology in Smart Manufacturing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 10 January 2025 | Viewed by 1793

Special Issue Editor

Dr. Sudha Ramasamy

E-Mail Website
Guest Editor
Högskolan Väst, Trollhattan, Sweden
Interests: robotics; path planning; multi agent systems; flexible automation

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) and sensing technology have revolutionized the landscape of smart manufacturing industries to achieve higher levels of efficiency, productivity, and automation with flexibilities. Flexibilities can be achieved via the Plug & Produce (P&P) concept with the help of multi-agent technology. AI and sensing technology are of utmost importance in smart manufacturing, especially in the context of Industry 4.0. AI drives the transformation of traditional manufacturing into smart manufacturing by integrating advanced algorithms and machine learning techniques with the manufacturing process. Sensing technology involves the use of sensors and Internet of Things (IoTs) to gather real-time information from the manufacturing process and environment; this revolution transforms traditional factories into intelligent and smart manufacturing. 

AI empowers manufacturers to leverage data and advance analytics by optimizing the energy and production schedule, improving the overall decision-making processes, and enhancing productivity to achieve higher levels of efficiency, flexibility, and sustainability in smart manufacturing. In addition to that, AI-powered automation systems can perform complex tasks with precision and adaptability, enhancing productivity, and reducing human errors. Sensing technology provides the necessary inputs and real-time data for the AI systems to operate effectively. Sensing technology information can be combined with AI-powered vision systems to enable visual inspection and the recognition of defects and abnormalities in the manufacturing process. The future of smart manufacturing holds great promise as AI and sensing technology continue to evolve, enabling more sophisticated decision making and autonomous control in the manufacturing industries.

This Special Issue seeks to collect the latest research and innovations concerning “Artificial Intelligence and Sensing Technology in Smart Manufacturing”.

Dr. Sudha Ramasamy
Guest Editor

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • smart manufacturing
  • machine vision
  • machine learning
  • artificial intelligence
  • internet of things
  • plug & produce
  • multi-agent systems
  • flexible manufacturing
  • process optimization
  • intelligent automation
  • energy efficiency

Published Papers (2 papers)

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Research

28 pages, 2880 KiB  
Article
Dynamic Intelligent Scheduling in Low-Carbon Heterogeneous Distributed Flexible Job Shops with Job Insertions and Transfers
by Yi Chen, Xiaojuan Liao, Guangzhu Chen and Yingjie Hou
Sensors 2024, 24(7), 2251; https://doi.org/10.3390/s24072251 - 31 Mar 2024
Viewed by 667
Abstract
With the rapid development of economic globalization and green manufacturing, traditional flexible job shop scheduling has evolved into the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP). Additionally, modern smart manufacturing processes encounter complex and diverse contingencies, necessitating the ability to address [...] Read more.
With the rapid development of economic globalization and green manufacturing, traditional flexible job shop scheduling has evolved into the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP). Additionally, modern smart manufacturing processes encounter complex and diverse contingencies, necessitating the ability to address dynamic events in real-world production activities. To date, there are limited studies that comprehensively address the intricate factors associated with the LHDFJSP, including workshop heterogeneity, job insertions and transfers, and considerations of low-carbon objectives. This paper establishes a multi-objective mathematical model with the goal of minimizing the total weighted tardiness and total energy consumption. To effectively solve this problem, diverse composite scheduling rules are formulated, alongside the application of a deep reinforcement learning (DRL) framework, i.e., Rainbow deep-Q network (Rainbow DQN), to learn the optimal scheduling strategy at each decision point in a dynamic environment. To verify the effectiveness of the proposed method, this paper extends the standard dataset to adapt to the LHDFJSP. Evaluation results confirm the generalization and robustness of the presented Rainbow DQN-based method. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technology in Smart Manufacturing)
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15 pages, 6917 KiB  
Article
Uncertainty Evaluation of a Gas Turbine Model Based on a Nonlinear Autoregressive Exogenous Model and Monte Carlo Dropout
by Armando Cajahuaringa, Rubén Aquize Palacios, Juan M. Mauricio Villanueva, Aurelio Morales-Villanueva, José Machuca, Juan Contreras and Kiara Rodríguez Bautista
Sensors 2024, 24(2), 465; https://doi.org/10.3390/s24020465 - 12 Jan 2024
Viewed by 715
Abstract
Gas turbines are thermoelectric plants with various applications, such as large-scale electricity production, petrochemical industry, and steam generation. In order to optimize the operation of a gas turbine, it is necessary to develop system identification models that allow for the development of studies [...] Read more.
Gas turbines are thermoelectric plants with various applications, such as large-scale electricity production, petrochemical industry, and steam generation. In order to optimize the operation of a gas turbine, it is necessary to develop system identification models that allow for the development of studies and analyses to increase the system’s reliability. Current strategies for modeling complex and non-linear systems can be based on artificial intelligence techniques, using autoregressive neural networks of the NARX and LSTM type. In this context, this work aims to develop a model of a gas turbine capable of estimating the rotation speed of the turbine and simultaneously estimating the uncertainty associated with the estimation. These methodologies are based on artificial neural networks and the Monte Carlo dropout simulation method. The results were obtained from experimental data from a 215 MW gas turbine, getting the best model with a MAPE of 0.02% and an uncertainty associated with the turbine rotation speed of 2.2 RPM. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technology in Smart Manufacturing)
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