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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 June 2025 | Viewed by 6370

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

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Published Papers (5 papers)

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Research

26 pages, 10620 KiB  
Article
Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation
by Zhigang Cai, Wangyang Li, Jianxin Song, Hongyu Jin and Hongya Fu
Sensors 2025, 25(6), 1742; https://doi.org/10.3390/s25061742 - 11 Mar 2025
Viewed by 364
Abstract
Accurately identifying tool wear states with variable cutting parameters can improve machining quality and efficiency. However, existing wear state recognition methods based on unsupervised domain adaptation mostly employ the knowledge transfer learning strategy in a single source domain. They cannot fully utilize the [...] Read more.
Accurately identifying tool wear states with variable cutting parameters can improve machining quality and efficiency. However, existing wear state recognition methods based on unsupervised domain adaptation mostly employ the knowledge transfer learning strategy in a single source domain. They cannot fully utilize the sensor data distribution information of multiple cutting parameters, hindering recognition performance improvement. Thus, this paper proposes a wear-state recognition method for variable cutting parameters based on multi-source unsupervised domain adaptation. First, non-stationary Transformer encoders extract non-stationary common features; then, sliced Wasserstein distance-based domain-specific feature distribution alignment and classifier output alignment scale down the domain shift and make multi-domain distribution synchronous alignment less complex. Finally, the milling experiments with variable cutting parameters are conducted to validate the recognition performance of the proposed method. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technology in Smart Manufacturing)
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23 pages, 958 KiB  
Article
Recipe Based Anomaly Detection with Adaptable Learning: Implications on Sustainable Smart Manufacturing
by Junhee Lee, Jaeseok Jang, Qing Tang and Hail Jung
Sensors 2025, 25(5), 1457; https://doi.org/10.3390/s25051457 - 27 Feb 2025
Viewed by 588
Abstract
The advent of Industry 4.0 has significantly transformed the manufacturing sector, bringing advancements in quality control efficiency, environmental sustainability, and production development. These changes have led to the development of intelligent technologies such as artificial intelligence (AI). However, implementing AI solutions in manufacturing [...] Read more.
The advent of Industry 4.0 has significantly transformed the manufacturing sector, bringing advancements in quality control efficiency, environmental sustainability, and production development. These changes have led to the development of intelligent technologies such as artificial intelligence (AI). However, implementing AI solutions in manufacturing processes still presents challenges in many aspects, particularly in handling irregular datasets influenced by diverse manufacturing settings. In the field of injection molding, quality inspection often occurs at the batch level rather than at the individual level, providing only the overall defect ratio of batch production instead of labeling each individual product. These issues limit the general application of AI and data-driven decision-making. To address these limitations and enhance product efficiency, this study proposes a novel anomaly detection framework for a specific manufacturing process. In Recipe-Based Learning, we first apply K-Means clustering to account for the flexible manufacturing process, which relies on diverse settings. The injection molding data are classified into setting-specific recipes to ensure data normality and uniqueness. The Kruskal-Wallis test is conducted to provide statistical evidence of differences in data based on varying settings, further justifying the necessity of Recipe-Based Learning. Then, Autoencoders for anomaly detection are trained with normal data from each recipe. With this data-driven AI approach, 61 defective products are predicted, compared to the existing 41 defects. Meanwhile, the integrated model, which does not consider variations in settings, only predicted 2 defects, indicating poor and distorted quality inspection. For Adaptable Learning, which focuses on new inputs with unseen settings, we apply KL-Divergence to identify the closest trained recipe data and its corresponding model. This approach outperformed both the integrated and additionally trained models in predictive power. As a result, continuous prediction is achieved without the need for further training, successfully enhancing process optimization. In the context of smart factories in the injection molding industry, such improvements in process management can significantly enhance overall productivity and decision-making, primarily through a data-driven AI approach. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technology in Smart Manufacturing)
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Graphical abstract

20 pages, 9783 KiB  
Article
A Lightweight and Efficient Multimodal Feature Fusion Network for Bearing Fault Diagnosis in Industrial Applications
by Chaoquan Mo, Ke Huang, Wenhan Li and Kaibo Xu
Sensors 2024, 24(22), 7139; https://doi.org/10.3390/s24227139 - 6 Nov 2024
Viewed by 1077
Abstract
To address the issues of single-structured feature input channels, insufficient feature learning capabilities in noisy environments, and large model parameter sizes in intelligent diagnostic models for mechanical equipment, a lightweight and efficient multimodal feature fusion convolutional neural network (LEMFN) method is proposed. Compared [...] Read more.
To address the issues of single-structured feature input channels, insufficient feature learning capabilities in noisy environments, and large model parameter sizes in intelligent diagnostic models for mechanical equipment, a lightweight and efficient multimodal feature fusion convolutional neural network (LEMFN) method is proposed. Compared with existing models, LEMFN captures rich fault features at multiple scales by combining time-domain and frequency-domain signals, thereby enhancing the model’s robustness to noise and improving data adaptability under varying operating conditions. Additionally, the convolutional block attention module (CBAM) and random overlapping sampling technology (ROST) are introduced, and through a feature fusion strategy, the accurate diagnosis of mechanical equipment faults is achieved. Experimental results demonstrate that the proposed method not only possesses high diagnostic accuracy and rapid convergence but also exhibits strong robustness in noisy environments. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed to promote the practical application of intelligent fault diagnosis in mechanical equipment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technology in Smart Manufacturing)
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26 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
Cited by 4 | Viewed by 2025
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
Cited by 2 | Viewed by 1529
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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