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Smart Sensing Technologies in Industry Applications
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Smart Sensing Technologies in Industry Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 1547

Special Issue Editors

Dr. Xiaotian Chen

E-Mail Website
Guest Editor
School of Mechanical Engineering, Sichuan University, Chengdu 610017, China
Interests: electro-magnetic thermography; image processing; smart sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Xuewu Dai

E-Mail Website
Guest Editor
Mathematics, Physics and Electrical Engineering, Northumbria Univerisity, Newcastle upon Tyne NE1 8ST, UK
Interests: networked control; intelligent scheduling;‬ industrial IoTs; electric vehicles

Special Issue Information

Dear Colleagues,

In recent years, smart sensing technologies have evolved remarkably, driven by significant advancements in areas ranging from flexible electronics to AI-assisted sensor systems. This Special Issue aims to showcase the latest research, developments, and applications within this rapidly expanding field.

We invite submissions addressing topics including, but not limited to, the following:

  • Flexible and stretchable sensors;
  • Smart and intelligent inspection systems;
  • Non-destructive testing (NDT);
  • Structural health monitoring (SHM);
  • Industrial and healthcare applications of smart sensors;
  • AI-based sensor data analysis and feature recognition.

We also strongly encourage contributions exploring emerging and interdisciplinary areas that will define the future of smart sensing, such as the following:

  • Self-powered and energy-harvesting sensors;
  • Sensor fusion and edge computing;
  • Sensor security and data privacy;
  • Sensor-driven digital twins.

This Special Issue seeks to present a comprehensive overview of current advancements and anticipated trends in smart sensing technologies. We believe that your contributions will highlight the essential role of sensing technologies as foundational components of AI-driven systems and digital twins, while proposing innovative solutions to contemporary challenges.

We look forward to your valuable submissions.

Dr. Xiaotian Chen
Dr. Xuewu Dai
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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 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

  • smart sensing
  • NDT
  • digital twins
  • SFM
  • sensor fusion & edge computing
  • industrial and healthcare applications
  • flexible Sensors
  • AI-based sensor data analysis

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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

21 pages, 4171 KB  
Article
Research on Laser Measurement Technology for Online Roll Profile Measurement in Strip Rolling Mills
by Jiankang Xing and Yan Peng
Appl. Sci. 2025, 15(24), 13101; https://doi.org/10.3390/app152413101 - 12 Dec 2025
Viewed by 55
Abstract
Online roll profile measurement technology enables real-time acquisition of the roll profile, guiding optimised roll change intervals to enhance production efficiency and product quality. To improve the accuracy of online roll profile measurement, this paper conducted research on laser measurement technology for online [...] Read more.
Online roll profile measurement technology enables real-time acquisition of the roll profile, guiding optimised roll change intervals to enhance production efficiency and product quality. To improve the accuracy of online roll profile measurement, this paper conducted research on laser measurement technology for online roll profile measurement in strip rolling mills. The factors influencing sensor measurement errors were analysed, and a protective housing for the sensor was designed. The experimental results showed that after installing this protective housing, the temperature fluctuation around the sensor was less than 0.2 °C, and the measurement error in a water environment was less than ±5 μm. The straightness error compensation model of the measurement system was established, and online roll profile measurement experiments were conducted on a four-high rolling mill in the laboratory. The experimental results indicated that the measured roll profile closely matched the actual roll profile, with a measurement error of less than 6.3 μm. This paper offers a novel approach to the study of online roll profile measurement technology. Full article
(This article belongs to the Special Issue Smart Sensing Technologies in Industry Applications)
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22 pages, 7352 KB  
Article
Micro-Computed Tomography Non-Destructive Testing and Defect Quantitative Analysis of Carbon Fiber-Reinforced Polymer, Glass Fiber-Reinforced Polymer and Carbon/Glass Hybrid Laminates Using Deep Learning Recognition
by Mingmeng Wang, Bo Zhang, Shiyu Zhan, Long Yang, Lanxin Jiang and Yujia Wang
Appl. Sci. 2025, 15(22), 12192; https://doi.org/10.3390/app152212192 - 17 Nov 2025
Viewed by 515
Abstract
X-ray micro-computed tomography (Micro-CT) is an advanced technique capable of non-destructive detection of internal defects in materials. Fiber-reinforced polymer (FRP) laminates are prone to forming defects such as pores during the manufacturing process, which significantly affect their mechanical properties. In this study, Micro-CT [...] Read more.
X-ray micro-computed tomography (Micro-CT) is an advanced technique capable of non-destructive detection of internal defects in materials. Fiber-reinforced polymer (FRP) laminates are prone to forming defects such as pores during the manufacturing process, which significantly affect their mechanical properties. In this study, Micro-CT technology was employed to conduct non-destructive testing on carbon fiber (CFRP), glass fiber (GFRP) and carbon/glass hybrid (C/G) laminates. Combined with the U-Net++ deep learning model, precise segmentation and three-dimensional reconstruction of pores were achieved. A systematic quantitative analysis was carried out on the distribution, size, volume and porosity of pores in six specimens with two layup angles (0/90 and ±45). The research results show that the pores in CFRP are mainly dispersed micro-pores and are relatively evenly distributed; the porosity of GFRP is the highest, and large interlaminar pores are prone to forming. The porosity fluctuates sharply in the thickness direction, revealing that the interlaminar interface is a defect-sensitive area. This provides a reliable quantitative basis and theoretical support for optimization and defect assessment. Full article
(This article belongs to the Special Issue Smart Sensing Technologies in Industry Applications)
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19 pages, 51881 KB  
Article
Spatiotemporal Analysis and Characterization of Multilayer Buried Cracks in Rails Using Swept-Frequency Eddy-Current-Pulsed Thermal Tomography
by Wei Qiao, Yanghanqi Liu, Jiahao Jiao, Xiaotian Chen and Hengbo Zhang
Appl. Sci. 2025, 15(16), 9069; https://doi.org/10.3390/app15169069 - 18 Aug 2025
Cited by 1 | Viewed by 694
Abstract
Rolling contact fatigue (RCF)-induced cracks in steel rails exhibit a fish-scale-shaped cluster distribution, and generally form in a layered, overlapping manner. Eddy-current-pulsed thermography (ECPT) has been applied in RCF detection by taking advantage of electromagnetic–thermal execution; however, one still faces challenges in identifying [...] Read more.
Rolling contact fatigue (RCF)-induced cracks in steel rails exhibit a fish-scale-shaped cluster distribution, and generally form in a layered, overlapping manner. Eddy-current-pulsed thermography (ECPT) has been applied in RCF detection by taking advantage of electromagnetic–thermal execution; however, one still faces challenges in identifying and quantifying such layered, overlapping defects. This paper proposes a swept-frequency eddy-current-pulsed thermal tomography (ECPTT) detection method to quantitatively characterize multilayer crack depth and inclination angle in an artificial rail sample. In particular, stimulating frequency modulation is used to guide the induced eddy current and heat to varying depths, and this is combined with principal component analysis (PCA) to identify multilayer defects. Moreover, a thermal signal reconstruction (TSR) algorithm is introduced. TSR features are extracted for analyzing the burial depth and inclination angle of multilayer defects. The results demonstrate that the third principal component (PC3), extracted via PCA, enables layer-count discrimination in multilayer defects. Integrated with gradient magnitude analysis of the second principal component (PC2) under swept-frequency excitation, defect contour localization error can be controlled within 0.5 mm. Building on layer discrimination, multi-frequency thermal response analysis further reveals variations in PC1’s variance contribution, differentiating inclination angles of 10° and 20°, whereas comparative heating- and cooling-rate magnitudes distinguish burial depths of 0.5 mm and 1.0 mm. The research verifies that the ECPTT system can accurately detect the layer number, inclination angle, and depth of buried RCF defects, substantially enhancing the accuracy of defect contour reconstruction. Full article
(This article belongs to the Special Issue Smart Sensing Technologies in Industry Applications)
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