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Applications of Manufacturing and Measurement Sensors: 2nd Edition
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Applications of Manufacturing and Measurement Sensors: 2nd Edition

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

Deadline for manuscript submissions: 20 July 2025 | Viewed by 2783

Special Issue Editors

Prof. Dr. Shichang Du

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: intelligent manufacturing; measurement application; quality control; manufacturing system; machine vision
Special Issues, Collections and Topics in MDPI journals
Dr. Yiping Shao

E-Mail Website
Guest Editor
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Interests: measurement and metrology; intelligent manufacturing; quality big data; visual inspection; monitoring and diagnosis; intelligent logistics equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Delin Huang

E-Mail Website
Guest Editor
School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China
Interests: surface monitoring; defect detection; point cloud data processing; machine vision; intelligent manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Measurement sensors are an essential technology in advanced manufacturing, with particuar significance in the context of engineering. With a tighter tolerance and higher performance standards, there is a need to develop fast and efficient applications of manufacturing and measurement sensors to satisfy the specifications of various products’ functional attributes.

As one of the experts in the field, I would like to cordially invite you to submit an original paper to this Special Issue, which we wish to publish to disseminate your brilliant ideas.

The aims and scope of this Special Issue include, but are not limited to, the following:

  • Advanced measurement sensors;
  • Measurement and sensing applications;
  • Manufacturing systems;
  • Applications of sensors in intelligent logistics equipment;
  • Quality monitoring and control;
  • Machine vision and applications;
  • Methods of detection.

Prof. Dr. Shichang Du
Dr. Yiping Shao
Dr. Delin Huang
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 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

  • advanced measurement sensors
  • measurement and sensing applications
  • manufacturing systems
  • applications of sensors in intelligent logistics equipment
  • quality monitoring and control
  • machine vision and applications
  • methods of detection

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (4 papers)

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Research

27 pages, 16583 KiB  
Article
Reinforcement Learning Approach to Optimizing Profilometric Sensor Trajectories for Surface Inspection
by Sara Roos-Hoefgeest, Mario Roos-Hoefgeest, Ignacio Álvarez and Rafael C. González
Sensors 2025, 25(7), 2271; https://doi.org/10.3390/s25072271 - 3 Apr 2025
Viewed by 255
Abstract
High-precision surface defect detection in manufacturing often relies on laser triangulation profilometric sensors for detailed surface measurements, providing detailed and accurate surface measurements over a line. Accurate motion between the sensor and workpiece, usually managed by robotic systems, is critical for maintaining optimal [...] Read more.
High-precision surface defect detection in manufacturing often relies on laser triangulation profilometric sensors for detailed surface measurements, providing detailed and accurate surface measurements over a line. Accurate motion between the sensor and workpiece, usually managed by robotic systems, is critical for maintaining optimal distance and orientation. This paper introduces a novel Reinforcement Learning (RL) approach to optimize inspection trajectories for profilometric sensors based on the boustrophedon scanning method. The RL model dynamically adjusts sensor position and tilt to ensure consistent profile distribution and high-quality scanning. We use a simulated environment replicating real-world conditions, including sensor noise and surface irregularities, to plan trajectories offline using CAD models. Key contributions include designing a state space, action space, and reward function tailored for profilometric sensor inspection. The Proximal Policy Optimization (PPO) algorithm trains the RL agent to optimize these trajectories effectively. Validation involves testing the model on various parts in simulation and performing real-world inspection with a UR3e robotic arm, demonstrating the approach’s practicality and effectiveness. Full article
(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors: 2nd Edition)
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Graphical abstract

13 pages, 8085 KiB  
Article
Feasibility Study on Additive Manufacturing of Feed Horn Operating in D-Band
by Giuseppe Addamo, Lorenzo Scalcinati, Mario Zannoni, Oscar Antonio Peverini and Flaviana Calignano
Sensors 2025, 25(2), 523; https://doi.org/10.3390/s25020523 - 17 Jan 2025
Viewed by 666
Abstract
This paper presents the outcomes of a feasibility study on the manufacturing of D-band horn antennas through the Low Power Bed Fusion process. Different prototypes have been realized and tested, showing nice results in terms of the co-polarization component. On the other hand, [...] Read more.
This paper presents the outcomes of a feasibility study on the manufacturing of D-band horn antennas through the Low Power Bed Fusion process. Different prototypes have been realized and tested, showing nice results in terms of the co-polarization component. On the other hand, a spurious cross-polarization component is present in the radiation pattern even in the principal planes, limiting the device to single-polarization applications. A mechanical study of the realized devices has been conducted on the horn internal channel to understand the reasons for this issue, and, subsequently, to address where to improve the manufacturing process. Full article
(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors: 2nd Edition)
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21 pages, 7674 KiB  
Article
Fatigue Experiment and Failure Mechanism Analysis of Aircraft Titanium Alloy Wing–Body Connection Joint
by Xianmin Chen, Shanshan Li, Yuanbo Liang, Shuo Wang, Liang Yan and Shichang Du
Sensors 2025, 25(1), 150; https://doi.org/10.3390/s25010150 - 30 Dec 2024
Viewed by 820
Abstract
Taking the titanium alloy wing–body connection joint at the rear beam of a certain type of aircraft as the research object, this study analyzed the failure mechanism and verified the structural safety of the wing–body connection joint under actual flight loads. Firstly, this [...] Read more.
Taking the titanium alloy wing–body connection joint at the rear beam of a certain type of aircraft as the research object, this study analyzed the failure mechanism and verified the structural safety of the wing–body connection joint under actual flight loads. Firstly, this study verified the validity of the loading system and the measuring system in the test system through the pre-test, and the repeatability of the test was analyzed for error to ensure the accuracy of the experimental data. Then, the test piece was subjected to 400,000 random load tests of flight takeoffs and landings, 100,000 Class A load tests, and ground–air–ground load tests, and the test piece fractured under the ground–air–ground load tests. Lastly, the mechanism analysis and structural safety verification of the fatigue fracture of the joints were carried out by using a stereo microscope and scanning electron microscope. The results show that fretting fatigue is the main driving force for crack initiation, and the crack shows significant fatigue damage characteristics in the stable growth stage and follows Paris’ law. Entering the final fracture region, the joint mainly experienced ductile fracture, with typical plastic deformation features such as dimples and tear ridges before fracture. The fatigue crack growth behavior of the joint was quantitatively analyzed using Paris’ law, and the calculated crack growth period life was 207,374 loadings. This result proves that the crack initiation life accounts for 95.19% of the full life cycle, which is much higher than the design requirement of 400,000 landings and takeoffs, indicating that the structural design of this test piece is on the conservative side and meets the requirements of aircraft operational safety. This research is of great significance in improving the safety and reliability of aircraft structures. Full article
(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors: 2nd Edition)
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23 pages, 9230 KiB  
Article
Error Separation Method for Geometric Distribution Error Modeling of Precision Machining Surfaces Based on K-Space Spectrum
by Zhichao Sheng, Jian Xiong, Zhijing Zhang, Taiyu Su, Min Zhang, Qimuge Saren and Xiao Chen
Sensors 2024, 24(24), 8067; https://doi.org/10.3390/s24248067 - 18 Dec 2024
Viewed by 757
Abstract
The geometric error distributed on components’ contact surfaces is a critical factor affecting assembly accuracy and precision instrument stability. Effective error separation methods can improve model accuracy, thereby aiding in performance prediction and process optimization. Here, an error separation method for geometric distribution [...] Read more.
The geometric error distributed on components’ contact surfaces is a critical factor affecting assembly accuracy and precision instrument stability. Effective error separation methods can improve model accuracy, thereby aiding in performance prediction and process optimization. Here, an error separation method for geometric distribution error modeling for precision machining surfaces based on the K-space spectrum is proposed. To determine the boundary of systematical error and random error, we used a cruciform boundary line method based on the K-space spectrum, achieving the optimal separation of the two with frequency difference. The effectiveness of the method was experimentally verified using two sets of machined surfaces. By comparing with current common random error filtering methods, the outstanding role of the proposed error separation method in separating random error and preserving processing features has been verified. Full article
(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors: 2nd Edition)
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