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Applications of Laser Sensors for Precision Measurements
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Applications of Laser Sensors for Precision Measurements

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 671

Special Issue Editor

Prof. Dr. Xinxiu Zhou

E-Mail Website
Guest Editor
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
Interests: magnetic suspension; inertial actuator control; quantum precision measurement

Special Issue Information

Dear Colleagues,

The rapid advancement of laser sensor technology has revolutionized the way we measure distance, time, object size, and speed across various industries. As the demand for precise and efficient measurement solutions grows in sectors such as manufacturing, robotics, and the automotive industry, the need to explore and enhance the capabilities of laser sensors has become increasingly crucial. Laser diodes are preferred for these sensors due to their high precision, quick response times, and ability to operate effectively over long distances.

This Special Issue focuses on all types of laser sensors based on laser diodes for measuring distance, time, object size, and speed.

Prof. Dr. Xinxiu Zhou
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

  • laser sensing technologies
  • high-precision measurement techniques
  • distance sensing applications
  • object dimensions measurement
  • velocity measurement systems

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Published Papers (1 paper)

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Research

27 pages, 15857 KB  
Article
Weld Defect Detection in Laser Beam Welding Using Multispectral Emission Sensor Features and Machine Learning
by Amena Darwish, Manfred Persson, Stefan Ericson, Rohollah Ghasemi and Kent Salomonsson
Sensors 2025, 25(16), 5120; https://doi.org/10.3390/s25165120 - 18 Aug 2025
Viewed by 349
Abstract
Laser beam welding (LBW) involves complex and rapid interactions between the laser and material, often resulting in defects such as pore formation. Emissions collected during the process offer valuable insight but are difficult to interpret directly for defect detection. In this study, we [...] Read more.
Laser beam welding (LBW) involves complex and rapid interactions between the laser and material, often resulting in defects such as pore formation. Emissions collected during the process offer valuable insight but are difficult to interpret directly for defect detection. In this study, we propose a data-driven framework to interpret electromagnetic emissions in LBW using both supervised and unsupervised learning. Our framework is implemented in the post-process monitoring stage and can be used as a real-time framework. The supervised approach uses labeled data corresponding to predefined defects (in this work, pore formation is an example of a defined defect). Meanwhile, the unsupervised method is used to identify anomalies without using predefined labels. Supervised and unsupervised learning aims to find reference values in the emissions data to determine the values of signals that lead to defects in welding (enabling quantitative monitoring). A total of 81 welding experiments were conducted, recording real-time emission data across 42 spectral channels. From these signals, statistical, temporal, and shape-based features were extracted, and dimensionality was reduced using Principal Component Analysis (PCA). The LSTM model achieved an average mean squared error (MSE) of 0.0029 and mean absolute error (MAE) of 0.0288 on the testing set across five folds. The Isolation Forest achieved 80% accuracy and 85.7% precision in detecting anomalous welds on a subset with validated defect labels. The proposed framework enhances the interpretability of 4D photonic data and enables both post-process analysis and potential real-time monitoring. It provides a scalable, data-driven approach to weld quality assessment for industrial applications. Full article
(This article belongs to the Special Issue Applications of Laser Sensors for Precision Measurements)
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