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Integrating Wireless Passive Sensor Technology Into Smart Manufacturing Education: A Teaching Framework From Theory to Practice.

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Title: Integrating Wireless Passive Sensor Technology Into Smart Manufacturing Education: A Teaching Framework From Theory to Practice.
Authors: Tao, Shunzhen1,2 (AUTHOR), Tu, Huating1,3 (AUTHOR), Liang, Kaihao1,2 (AUTHOR), Gao, Yang1,2 (AUTHOR) yanggao@ecust.edu.cn, Zhang, Jianrui1,2 (AUTHOR) jrzhang@ecust.edu.cn, Zhang, Yang4 (AUTHOR), Xuan, Fuzhen1,2 (AUTHOR) fzxuan@ecust.edu.cn
Source: Computer Applications in Engineering Education. May2026, Vol. 34 Issue 3, p1-14. 14p.
Subjects: Curriculum frameworks, Three-dimensional printing, Microwave remote sensing, Computational electromagnetics, Detectors, Industry 4.0, Rapid prototyping, Neural architecture search
Abstract: With the rapid development of intelligent manufacturing, it is imperative that the engineering education system urgently needs to evolve in sync with cutting‐edge industrial technologies. The industrial sector has widely adopted advanced technologies such as wireless passive sensors (WPS), but the content of relevant courses in universities still lags behind technological development, especially in the integration of new sensing mechanisms such as WPS into mechatronics and sensor courses, which have obvious shortcomings. To fill this gap, a comprehensive teaching framework that integrates "Theory, Design, Preparation, Testing" was proposed in this article. This framework integrates the basic theory of microwave sensing, electromagnetic simulation practice, intelligent optimization design based on transformer neural network, convenient digital manufacturing methods, and actual sensing testing, aiming to help students systematically master the full process capability of WPS from theory to practice through a layered and progressive teaching path. The introduction of encoding structures and neural network prediction methods significantly improves the efficiency and effectiveness of sensor performance optimization, enabling students to experience a paradigm shift from traditional empirical design to data‐driven intelligent design. At the same time, by replacing traditional MEMS processes with 3D printing technology, the complexity, costness, and difficulties to implement micro‐nano processing equipment have been effectively solved, achieving rapid closed‐loop verification from concept to physical object. The effectiveness of this framework has been verified through multi‐dimensional student feedback. The framework not only cultivates students' interdisciplinary integration abilities in the fields of digital manufacturing and industrial IoT, but also provides an extensible teaching practice path for engineering education to adapt to technological development. [ABSTRACT FROM AUTHOR]
Copyright of Computer Applications in Engineering Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Integrating Wireless Passive Sensor Technology Into Smart Manufacturing Education: A Teaching Framework From Theory to Practice.
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  Data: <searchLink fieldCode="JN" term="%22Computer+Applications+in+Engineering+Education%22">Computer Applications in Engineering Education</searchLink>. May2026, Vol. 34 Issue 3, p1-14. 14p.
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  Data: With the rapid development of intelligent manufacturing, it is imperative that the engineering education system urgently needs to evolve in sync with cutting‐edge industrial technologies. The industrial sector has widely adopted advanced technologies such as wireless passive sensors (WPS), but the content of relevant courses in universities still lags behind technological development, especially in the integration of new sensing mechanisms such as WPS into mechatronics and sensor courses, which have obvious shortcomings. To fill this gap, a comprehensive teaching framework that integrates "Theory, Design, Preparation, Testing" was proposed in this article. This framework integrates the basic theory of microwave sensing, electromagnetic simulation practice, intelligent optimization design based on transformer neural network, convenient digital manufacturing methods, and actual sensing testing, aiming to help students systematically master the full process capability of WPS from theory to practice through a layered and progressive teaching path. The introduction of encoding structures and neural network prediction methods significantly improves the efficiency and effectiveness of sensor performance optimization, enabling students to experience a paradigm shift from traditional empirical design to data‐driven intelligent design. At the same time, by replacing traditional MEMS processes with 3D printing technology, the complexity, costness, and difficulties to implement micro‐nano processing equipment have been effectively solved, achieving rapid closed‐loop verification from concept to physical object. The effectiveness of this framework has been verified through multi‐dimensional student feedback. The framework not only cultivates students' interdisciplinary integration abilities in the fields of digital manufacturing and industrial IoT, but also provides an extensible teaching practice path for engineering education to adapt to technological development. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Applications in Engineering Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/cae.70195
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        Text: English
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        PageCount: 14
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      – SubjectFull: Microwave remote sensing
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      – SubjectFull: Computational electromagnetics
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      – SubjectFull: Detectors
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      – SubjectFull: Industry 4.0
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      – SubjectFull: Rapid prototyping
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      – SubjectFull: Neural architecture search
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      – TitleFull: Integrating Wireless Passive Sensor Technology Into Smart Manufacturing Education: A Teaching Framework From Theory to Practice.
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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