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Machines
Special Issues
Recent Applications in Non-destructive Testing (NDT)
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Recent Applications in Non-destructive Testing (NDT)

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Material Processing Technology".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 2081

Special Issue Editor

Dr. Qiuji Yi

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Bristol, Bristol BS8 1TH, UK
Interests: electreomagnetic NDT; composites modelling and testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Non-destructive testing (NDT) has come to play a crucial role in a variety of industries, including the fields of manufacturing, aerospace, automotive, and construction. NDT techniques enable the inspection of materials and components, allowing for quality control, failure analysis, and preventive maintenance.

In this proposed Special Issue, we aim to present recent applications of NDT techniques and overview their impact on various industries. The Special Issue will cover a broad range of topics, including ultrasonic testing, eddy current testing, and radiographic testing. We particaulaly welcome applications of novel NDT techniques to challenging material such as composites, additive layer materials and recycle/ resued materials.

The Special Issue will be of interest to researchers, engineers, and industry professionals who are involved in the development and application of NDT techniques. The papers collated in this Special Issue will provide insights into the latest NDT trends and innovations, highlight the challenges and limitations of current NDT techniques, and present practical solutions for improving NDT methods.

Machines focuses on the latest developments in machines, their design, and their operation, making it an ideal platform for the dissemination of research on NDT techniques and their applications. By publishing research on NDT techniques, this Special Issue aims to contribute to the development sustainable maunfacturing enabled by NDT, supporting the global transition towards more sustainable and responsible industrial practices.

Dr. Qiuji Yi
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. Machines is an international peer-reviewed open access monthly 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 2400 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.

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Published Papers (1 paper)

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Research

33 pages, 8698 KiB  
Article
Welding Penetration Monitoring for Ship Robotic GMAW Using Arc Sound Sensing Based on Improved Wavelet Denoising
by Ziquan Jiao, Tongshuai Yang, Xingyu Gao, Shanben Chen and Wenjing Liu
Machines 2023, 11(9), 911; https://doi.org/10.3390/machines11090911 - 16 Sep 2023
Cited by 2 | Viewed by 1507
Abstract
The arc sound signal is one of the most important aspects of information related to pattern identification regarding the penetration state of ship robotic GMAW; however, arc sound is inevitably affected by noise interference during the signal acquisition process. In this paper, an [...] Read more.
The arc sound signal is one of the most important aspects of information related to pattern identification regarding the penetration state of ship robotic GMAW; however, arc sound is inevitably affected by noise interference during the signal acquisition process. In this paper, an improved wavelet threshold denoising method is proposed to eliminate interference and purify the arc sound signal. The non-stationary random distribution characteristics of GMAW noise interference are also estimated by using the high-frequency detail coefficients in different domains after wavelet transformation, and a mode of measuring scale that is logarithmically negatively correlated with the wavelet decomposition scale is created to update the threshold. The gradient convergent threshold function is established using the natural logarithmic function structure and concave–convex gradient to enable the nonlinear adjustment of the asymptotic rate. Further, some property theorems related to the optimized threshold function are proposed and theoretically proven, and the effectiveness and adaptability of the improved method are verified via the denoising simulation of speech synthesis signals. The four traditional denoising methods and our improved version are applied in the pretreatment of the GMAW arc sound signal, respectively. Statistical analysis and short-time Fourier transform are used to extract eight-dimensional time and frequency domain feature parameters from the denoised signals with randomly time-varying characteristics, and the extracted joint feature parameters are used to establish a nonlinear mapping model of penetration state identification for ship robotic GMAW using the pattern classifiers of RBFNN, PNN and PSO-SVM. The simulation results yielded by visual penetration classification and the multi-dimensional evaluation index of the confusion matrix indicate that the improved denoising method proposed in this paper achieves a higher accuracy in the extraction of penetration state features and greater precision in the identification of pattern classification. Full article
(This article belongs to the Special Issue Recent Applications in Non-destructive Testing (NDT))
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