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Sensing in Intelligent and Unmanned Additive Manufacturing
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Sensing in Intelligent and Unmanned Additive Manufacturing

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 670

Special Issue Editors

Dr. Longxi Luo

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100080, China
Interests: intelligent human–machine systems; intelligent driving assistance; intelligent multi-source information perception; intelligent human–machine interaction and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Changmeng Liu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: intelligent additive manufacturing
Dr. Yueling Guo

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: intelligent additive manufacturing

Special Issue Information

Dear Colleagues,

This Special Issue on Sensing in Intelligent and Unmanned Additive Manufacturing delves into the exponential growth and promising future of additive manufacturing technology. Despite the rapid advancements in this field, the production of large and intricate structures with optimal efficiency and quality remains a challenge. The emergence of intelligent and unmanned additive manufacturing is anticipated to not only transform manufacturing processes but also drive its widespread industrial adoption on a monumental scale. Sensing technology serves as the cornerstone in facilitating intelligent additive manufacturing systems by furnishing critical data for real-time monitoring and precise control of the manufacturing process.

This Special Issue is dedicated to exploring a spectrum of sensing technologies, methodologies, and algorithms that are pivotal in propelling the evolution of intelligent and unmanned additive manufacturing. By delving into these fundamental aspects, researchers and industry professionals are presented with a unique opportunity to gain profound insights into the innovation and implementation of cutting-edge sensing solutions tailored specifically for additive manufacturing processes. Through this comprehensive examination, this Special Issue aims to catalyze advancements in the field and pave the way for a future where intelligent and unmanned additive manufacturing becomes the norm rather than the exception.

Dr. Longxi Luo
Prof. Dr. Changmeng Liu
Dr. Yueling Guo
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

  • optical sensors and computer vision algorithms in additive manufacturing (AM)
  • 3D quality monitoring and structure reconstruction
  • other sensing technologies in additive manufacturing
  • digital twins for additive manufacturing
  • edge computation technologies for sensing and control
  • end-to-end sensing and control algorithms
  • solutions for small-sample learning for sensing and perception in challenging conditions
  • model pruning and distillation for real-time sensing and perception
  • FEM based on model reconstruction of real structures
  • sensors and internet of things

Published Papers (1 paper)

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Research

16 pages, 7866 KiB  
Article
Acoustic Signal-Based Defect Identification for Directed Energy Deposition-Arc Using Wavelet Time–Frequency Diagrams
by Hui Zhang, Qianru Wu, Wenlai Tang and Jiquan Yang
Sensors 2024, 24(13), 4397; https://doi.org/10.3390/s24134397 - 7 Jul 2024
Viewed by 484
Abstract
Several advantages of directed energy deposition-arc (DED-arc) have garnered considerable research attention including high deposition rates and low costs. However, defects such as discontinuity and pores may occur during the manufacturing process. Defect identification is the key to monitoring and quality assessments of [...] Read more.
Several advantages of directed energy deposition-arc (DED-arc) have garnered considerable research attention including high deposition rates and low costs. However, defects such as discontinuity and pores may occur during the manufacturing process. Defect identification is the key to monitoring and quality assessments of the additive manufacturing process. This study proposes a novel acoustic signal-based defect identification method for DED-arc via wavelet time–frequency diagrams. With the continuous wavelet transform, one-dimensional (1D) acoustic signals acquired in situ during manufacturing are converted into two-dimensional (2D) time–frequency diagrams to train, validate, and test the convolutional neural network (CNN) models. In this study, several CNN models were examined and compared, including AlexNet, ResNet-18, VGG-16, and MobileNetV3. The accuracy of the models was 96.35%, 97.92%, 97.01%, and 98.31%, respectively. The findings demonstrate that the energy distribution of normal and abnormal acoustic signals has significant differences in both the time and frequency domains. The proposed method is verified to identify defects effectively in the manufacturing process and advance the identification time. Full article
(This article belongs to the Special Issue Sensing in Intelligent and Unmanned Additive Manufacturing)
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