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Advanced Sensing for Smart Precision Manufacturing

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 4133

Special Issue Editors


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Guest Editor
State Key Laboratory of Ultra-Precision Machining Technology, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Interests: precision engineering; ultra-precision machining technology; precision metrology
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Special Issue Information

Dear Colleagues,

With the rapid growth in the demand for advanced products and high-precision components for various functional applications in fields such as aerospace, biomedical, advanced optics, photonics and telecommunication, etc., the smart precision manufacturing concepts of Industry 4.0 shed some light and provide important means for the manufacture of those products. Industry 4.0 first originated in Germany for achieving a new level of operational efficiency, productivity and automation. There are several factors which contribute to the successful implementation of smart precision manufacturing based on the Industrial 4.0 concept. One of them is the need for smart equipment and instruments implemented with real-time sensing systems. Research for key technologies for advanced sensing is indispensable for in-process data acquisition and subsequent data analysis and monitoring of the precision manufacturing processes. This Special Issue aims to provide a good collection of the latest research results and findings concerning recent advances in advanced sensing technology for precision smart manufacturing. Potential topics include, but are not limited to, the following:

  • Multi-sensor and data fusion;
  • Industrial Internet-of-things (IIoT);
  • Smart manufacturing;
  • Intelligent systems;
  • Machine Learning;
  • In situ measurement;
  • Self-optimization;
  • Real-time process monitoring;
  • Machine vision and image processing;
  • Smart Sensors and instrumentation;
  • Multi-objective optimization;
  • Digital twin;
  • Non-destructive testing.

Prof. Dr. Benny C. F. Cheung
Prof. Dr. Jiang Guo
Guest Editors

Manuscript Submission Information

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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

  • Multi-sensor and data fusion;
  • Industrial Internet-of-things (IIoT);
  • Smart manufacturing;
  • Intelligent systems;
  • Machine Learning;
  • In situ measurement;
  • Self-optimization;
  • Real-time process monitoring;
  • Machine vision and image processing;
  • Smart Sensors and instrumentation;
  • Multi-objective optimization;
  • Digital twin;
  • Non-destructive testing.

Published Papers (3 papers)

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Research

11 pages, 4021 KiB  
Article
Autostereoscopic 3D Measurement Based on Adaptive Focus Volume Aggregation
by Sanshan Gao and Chi Fai Cheung
Sensors 2023, 23(23), 9419; https://doi.org/10.3390/s23239419 - 26 Nov 2023
Viewed by 704
Abstract
Autostereoscopic three-dimensional measuring systems are a kind of portable and fast precision metrology instrument. The systems are based on integral imaging that makes use of a micro-lens array before an image sensor to observe measured parts from multiple perspectives. Since autostereoscopic measuring systems [...] Read more.
Autostereoscopic three-dimensional measuring systems are a kind of portable and fast precision metrology instrument. The systems are based on integral imaging that makes use of a micro-lens array before an image sensor to observe measured parts from multiple perspectives. Since autostereoscopic measuring systems can obtain longitudinal and lateral information within single snapshots rapidly, the three-dimensional profiles of the measured parts can be reconstructed by shape from focus. In general, the reconstruction process consists of data acquisition, pre-processing, digital refocusing, focus measures, and depth estimation. The accuracy of depth estimation is determined by the focus volume generated by focus measure operators which could be sensitive to the noise during digital refocusing. Without prior knowledge and surface information, directly estimated depth maps usually contain severe noise and incorrect representation of continuous surfaces. To eliminate the effects of refocusing noise and take advantage of traditional focus measure methods with robustness, an adaptive focus volume aggregation method based on convolutional neural networks is presented to optimize the focus volume for more accurate depth estimation. Since a large amount of data and ground truth are costly to acquire for model convergence, backpropagation is performed for every sample under an unsupervised strategy. The training strategy makes use of a smoothness constraint and an identical distribution constraint that restricts the difference between the distribution of the network output and the distribution of ideal depth estimation. Experimental results show that the proposed adaptive aggregation method significantly reduces the noise during depth estimation and retains more accurate surface profiles. As a result, the autostereoscopic measuring system can directly recover surface profiles from raw data without any prior information. Full article
(This article belongs to the Special Issue Advanced Sensing for Smart Precision Manufacturing)
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14 pages, 12958 KiB  
Article
Optimization of Tungsten Heavy Alloy Cutting Parameters Based on RSM and Reinforcement Dung Beetle Algorithm
by Xu Zhu, Chao Ni, Guilin Chen and Jiang Guo
Sensors 2023, 23(12), 5616; https://doi.org/10.3390/s23125616 - 15 Jun 2023
Cited by 5 | Viewed by 1587
Abstract
Tungsten heavy alloys (WHAs) are an extremely hard-to-machine material extensively used in demanding applications such as missile liners, aerospace, and optical molds. However, the machining of WHAs remains a challenging task as a result of their high density and elastic stiffness which lead [...] Read more.
Tungsten heavy alloys (WHAs) are an extremely hard-to-machine material extensively used in demanding applications such as missile liners, aerospace, and optical molds. However, the machining of WHAs remains a challenging task as a result of their high density and elastic stiffness which lead to the deterioration of the machined surface roughness. This paper proposes a brand-new multi-objective dung beetle algorithm. It does not take the cutting parameters (i.e., cutting speed, feed rate, and depth of cut) as the optimization objects but directly optimizes cutting forces and vibration signals monitored using a multi-sensor (i.e., dynamometer and accelerometer). The cutting parameters in the WHA turning process are analyzed through the use of the response surface method (RSM) and the improved dung beetle optimization algorithm. Experimental verification shows that the algorithm has better convergence speed and optimization ability compared with similar algorithms. The optimized forces and vibration are reduced by 9.7% and 46.47%, respectively, and the surface roughness Ra of the machined surface is reduced by 18.2%. The proposed modeling and optimization algorithms are anticipated to be powerful to provide the basis for the parameter optimization in the cutting of WHAs. Full article
(This article belongs to the Special Issue Advanced Sensing for Smart Precision Manufacturing)
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14 pages, 37790 KiB  
Article
In-Mould OCT Sensors Combined with Piezo-Actuated Positioning Devices for Compensating for Displacement in Injection Overmoulding of Optoelectronic Parts
by Günther Hannesschläger, Martin Schwarze, Elisabeth Leiss-Holzinger and Christian Rankl
Sensors 2023, 23(6), 3242; https://doi.org/10.3390/s23063242 - 19 Mar 2023
Viewed by 1187
Abstract
When overmoulding optoelectronic devices with optical elements, precise alignment of the overmoulded part and the mould is of great importance. However, mould-integrated positioning sensors and actuators are not yet available as standard components. As a solution, we present a mould-integrated optical coherence tomography [...] Read more.
When overmoulding optoelectronic devices with optical elements, precise alignment of the overmoulded part and the mould is of great importance. However, mould-integrated positioning sensors and actuators are not yet available as standard components. As a solution, we present a mould-integrated optical coherence tomography (OCT) device that is combined with a piezo-driven mechatronic actuator, which is capable of performing the necessary displacement correction. Because of the complex geometric structure optoelectronic devices may have, a 3D imaging method was preferable, so OCT was chosen. It is shown that the overall concept leads to sufficient alignment accuracy and, apart from compensating for the in-plane position error, provides valuable additional information about the sample both before and after the injection process. The increased alignment accuracy leads to better energy efficiency, improved overall performance and less scrap parts, and thus even a zero-waste production process might be feasible. Full article
(This article belongs to the Special Issue Advanced Sensing for Smart Precision Manufacturing)
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