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Measurement Sensors and Machine Learning Applications in Modern Manufacturing

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

Deadline for manuscript submissions: 15 September 2026 | Viewed by 6156

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Special Issue Editors

Dr. Tomasz Mazur

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Guest Editor

Faculty of Mechanical Engineering, Casimir Pulaski Radom University, ul. Stasieckiego 54, 26-600 Radom, Poland
Interests: metrology; industrial measurements; CMM

Prof. Dr. Mirosław Rucki

E-Mail Website
Guest Editor

Institute of Mechanical Science, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania
Interests: metrology; measurement systems
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Special Issue Information

Dear Colleagues,

This Special Issue (SI) is devoted to any kind of measurement techniques and instrumentation involved in modern manufacturing processes including, but not limited to, dimensional measurement, form deviation measurement, surface topography, characterization of materials structures, measurement of micro- and nanostructural features, tribological and other material testing methods, various applications of sensors and gauges, and measurement data processing, especially those involving Artificial Intelligence, Neural Networks, and Machine Learning algorithms. In particular, manuscripts regarding physical sensors, remote sensors, smart sensors, sensor networks, sensor principles, and industrial applications, which are within the scope of Sensors, are of special interest for this SI. All high-quality papers presenting experimental results, simulations, theoretical research, comparative analyses, and innovative approaches concerning measurement devices and techniques, especially the ones emphasizing control of measurement procedures and advanced data processing methods, are welcome.

Dr. Tomasz Mazur
Prof. Dr. Mirosław Rucki
Guest Editors

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Keywords

sensors
gauges
calibration
measurement
surface topography
coordinate measurement
manufacturing
machine learning
measurement data processing

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Published Papers (6 papers)

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Research

24 pages, 5686 KB

Open AccessArticle

3D Simulation Study for a Pneumatic Nozzle–Cylindrical Flapper System

by Peimin Xu, Kazuaki Inaba and Toshiharu Kagawa

Sensors 2026, 26(9), 2578; https://doi.org/10.3390/s26092578 - 22 Apr 2026

Viewed by 448

Abstract

With the increasing demand for higher efficiency in semiconductor machining, air spindles with compensation systems have attracted growing attention. The pneumatic nozzle–cylindrical flapper is a promising sensing approach due to its high precision and suitability for displacement measurement of high-speed rotating bodies. However, its complex three-dimensional flow behavior leads to significant deviations from conventional nozzle–flat flapper models, limiting its practical application. This study aims to clarify the flow mechanisms governing the nozzle–cylindrical flapper system and to establish a reliable framework for predicting its static characteristics. A computational fluid dynamics model is developed to analyze gas flow within the micron-scale clearance under varying gap sizes and angular orientations, and the results are validated against experimental measurements. The analysis shows that curvature plays a dominant role in the flow behavior. Increasing curvature enhances inertia-driven acceleration and weakens viscous effects while simultaneously inducing strong recirculation due to flow wrapping around the cylindrical surface. These competing mechanisms explain the observed deviations from conventional models and cannot be captured by two-dimensional approaches. Based on the numerical results, a mass flow rate compensation coefficient is introduced and correlated with the momentum compensation coefficient. A quadratic relationship between the two coefficients is identified, indicating a common recirculation-driven mechanism. These findings support previous semi-empirical assumptions and provide a basis for predicting static characteristics with reduced reliance on experimental calibration. Full article

(This article belongs to the Special Issue Measurement Sensors and Machine Learning Applications in Modern Manufacturing)

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19 pages, 7223 KB

Open AccessArticle

Accurate Lens-Distortion Measurement Through Detector Nyquist Sampling

by Yongqiang Yang, Zhiyi Wang, Junlin Li, Zhongming Li, Jianlin Lv, Min Zhao, Yanfu Tang and Jianli Wang

Sensors 2026, 26(5), 1550; https://doi.org/10.3390/s26051550 - 1 Mar 2026

Viewed by 425

Abstract

Distortion is a key parameter affecting the imaging performance of lenses. In this study, we propose a testing method based on detector Nyquist sampling of image data to achieve high-precision measurements of the distortion distribution of lenses. The distribution patterns of distortions in horizontal and vertical directions can be obtained by analyzing the distribution patterns of Moiré fringes in images under Nyquist sampling conditions and using phase-shift algorithms. The distortion-distribution characteristics of the lens are then calculated using distortion formulas. This method is characterized by high testing accuracy and sampling resolution. The image-plane distortion distribution exhibited a consistent linear trend when the object-plane position varied within a limited spatial range. Furthermore, the proposed method achieved a magnification deviation factor repeatability accuracy of approximately ±108 nm/cm and third-order distortion-measurement accuracy of approximately ±108 nm/cm³. This method enables a high-precision distortion evaluation of conventional industrial imaging lenses. Full article

(This article belongs to the Special Issue Measurement Sensors and Machine Learning Applications in Modern Manufacturing)

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16 pages, 4801 KB

Open AccessArticle

Welding Seam Recognition and Trajectory Planning Based on Deep Learning in Electron Beam Welding

by Hao Yang, Congjin Zuo, Haiying Xu and Xiaofei Xu

Sensors 2026, 26(2), 641; https://doi.org/10.3390/s26020641 - 18 Jan 2026

Viewed by 775

Abstract

To address challenges in weld recognition during vacuum electron beam welding caused by dark environments and metal reflections, this study proposes an improved hybrid algorithm combining YOLOv11-seg with adaptive Canny edge detection. By incorporating the UFO-ViT attention mechanism and optimizing the network architecture with the EIoU loss function, along with adaptive threshold setting for the Canny operator using the Otsu method, the recognition performance under complex conditions is significantly enhanced. Experimental results demonstrate that the optimized model achieves an average precision (mAP) of 77.4%, representing a 9-percentage-point improvement over the baseline YOLOv11-seg. The system operates at 20 frames per second (FPS), meeting real-time requirements, with the generated welding trajectories showing an average length deviation of less than 3 mm from actual welds. This approach provides an effective pre-weld visual guidance solution, which is a critical step towards the automation of electron beam welding. Full article

(This article belongs to the Special Issue Measurement Sensors and Machine Learning Applications in Modern Manufacturing)

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25 pages, 2615 KB

Open AccessArticle

Research on Low-Cost Non-Contact Vision-Based Wheel Arch Detection for End-of-Line Stage

by Zhigang Ding, Mingsheng Lin, Yi Ding, Yun Li and Qincheng Zhang

Sensors 2026, 26(1), 234; https://doi.org/10.3390/s26010234 - 30 Dec 2025

Viewed by 688

Abstract

To address the collaborative requirements of high precision, high efficiency, low cost, and non-contact measurement for wheel arch detection in the calibration of Advanced Driver Assistance Systems (ADAS) during vehicle production, this study proposes a monocular machine vision-based detection methodology. The hardware system incorporates an industrial camera, priced at approximately 1000 CNY, and a custom light source. The YOLOv5s model is employed for rapid localization of the wheel hub, while the MSER algorithm, in conjunction with Canny edge detection, is utilized for robust feature extraction of the wheel arch. A geometric computation model, referenced to the wheel hub, is subsequently established to quantify the wheel arch height. Experimental results indicate that, for seven vehicle models, the method achieves an average absolute error (MAE) of ≤0.25 mm, with a maximum error of ≤0.545 mm and a single measurement time of ≤3.2 s, making it suitable for a 60 JPH production line. Additionally, under lighting conditions ranging from 500 to 1500 lux and dust concentrations of ≤10 mg/m³, the MAE fluctuation remains within ≤0.08 mm, ensuring consistent measurement accuracy. This methodology offers a cost-effective, reliable, and fully automated solution for wheel arch detection in ADAS calibration, demonstrating strong adaptability to production lines and considerable potential for industrial applications. Full article

(This article belongs to the Special Issue Measurement Sensors and Machine Learning Applications in Modern Manufacturing)

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19 pages, 2054 KB

Open AccessArticle

Multi-Task Deep Learning for Surface Metrology

by Dawid Kucharski, Adam Gąska, Tomasz Kowaluk, Krzysztof Stępień, Marta Rępalska, Bartosz Gapiński, Michal Wieczorowski, Michal Nawotka, Piotr Sobecki, Piotr Sosinowski, Jan Tomasik and Adam Wójtowicz

Sensors 2025, 25(24), 7471; https://doi.org/10.3390/s25247471 - 8 Dec 2025

Cited by 1 | Viewed by 1319

Abstract

A reproducible deep learning framework is presented for surface metrology to predict surface texture parameters together with their reported standard uncertainties. Using a multi-instrument dataset spanning tactile and optical systems, we jointly address measurement system type classification and regression of key surface parameters—arithmetic mean roughness (Ra), mean peak-to-valley roughness (Rz), and total roundness deviation (RONt)—alongside their reported standard uncertainties. Uncertainty is modelled via quantile and heteroscedastic regression heads, with post hoc conformal calibration used to obtain calibrated prediction intervals. On a held-out test set, high fidelity was achieved by single-target regressors (coefficients of determination: Ra 0.9824, Rz 0.9847, RONt 0.9918), with two uncertainty targets also well modelled (standard uncertainty of Ra 0.9899, standard uncertainty of Rz 0.9955); the standard uncertainty of RONt remained more difficult to learn (0.4934). The classifier reached 92.85% accuracy, and probability calibration was essentially unchanged after temperature scaling (expected calibration error 0.00504 → 0.00503 on the test split). Negative transfer was observed for naive multi-output trunks, with single-target models performing better. These results provide calibrated predictions suitable for informing instrument selection and acceptance decisions in metrological workflows. Full article

(This article belongs to the Special Issue Measurement Sensors and Machine Learning Applications in Modern Manufacturing)

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17 pages, 3594 KB

Open AccessArticle

Statistical Analysis of Digital 3D Models of a Fossil Tetrapod Skull from µCT and Optical Scanning

by Yaroslav Garashchenko, Ilja Kogan and Miroslaw Rucki

Sensors 2025, 25(19), 6084; https://doi.org/10.3390/s25196084 - 2 Oct 2025

Cited by 1 | Viewed by 1717

Abstract

The quality of digital 3D models of fossils is important from the perspective of their further usage, either for scientific or didactical purposes. However, fidelity evaluation has rarely been attempted for digitized fossil objects. In the present research, a 3D triangulated model of the unique skull of Madygenerpeton pustulatum was built using an YXLON µCT device. The comparative analysis was performed using models obtained from seven optical surface-scanning systems. Methodology for accuracy assessment involved the determination of distances between the points in pairs of models, interchanging the reference and tested ones. Statistical significance testing using paired t-tests was performed. In particular, it was found that the YXLON µCT model was closest to the one obtained from AICON SmartScan, exhibiting an average distance of

\bar{∆_{d}}

= −0.0183 mm with a standard deviation of σ{∆_d} = 0.0778 mm, which is close to the permissible error of 20 µm given in technical specifications for AICON scanners. It was demonstrated that the analysis maintained measurement validity even though the YXLON model consisted of 23.8 M polygons and the AICON model consisted of 13.9 M polygons. Comparison with other digital models demonstrated that the fidelity of the triangulated µCT model made it feasible for further research and dissemination purposes. Full article

(This article belongs to the Special Issue Measurement Sensors and Machine Learning Applications in Modern Manufacturing)

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