Special Issue "Advanced Non-Destructive Testing in Steels"

A special issue of Metals (ISSN 2075-4701).

Deadline for manuscript submissions: 31 October 2017

Special Issue Editor

Guest Editor
Prof. Dr. Evangelos Hristoforou

Laboratory of Electronic Sensors, National Technical University of Athens (NTUA),Zografou Campus, Athens 15780, Greece
Website | E-Mail
Phone: +30-2107722178
Interests: magnetism and magnetic materials; magnetostriction and magnetostrictive sensors; magnetometers, magnetic steel heath monitoring; magnetically aided production of hydrogen; selective magnetic separation; magnetic powders; magnetic particle imaging; magnetic theragnostics

Special Issue Information

Dear Colleagues,

Non-destructive testing (NDT) is a major issue for industrial and bio-medical applications, with several tens of billions USD in annual turnover. The technology in NDT is rapidly improving due to the development of new technologies, e.g., sensors, electronics, communications, software applications, and integration processes.

The NDT methods for steel applications are following this general trend. Year-by-year, the methods and processes become better and better, with the current state-of-the-art being the accurate detection of flaws and defects in steels in the order of microns.

The vision in steel production and manufacturing industry is the real time monitoring of the stress tensor distribution in steels, with the stress gradient components providing the metrics for steel failure prediction. Such an online measurement, in combination with the existing instruments monitoring flaws and defects in steels, can be the feedback tool for steel production and manufacturing lines, for optimizing the quality of steel products.

Apart from steel producers and manufacturers, the applications of stress gradient monitoring extend to the energy sector, concerning steel structures in both thermal and nuclear stations; the oil and gas industry piping systems and vessels under pressure; the transportation sector including shipping and ship industry as well as rails, trains, automobiles and finally in all different types of constructions involving steel.

The aim of this Special Issue is to present, on the one hand, the advances in non-destructive methods and instruments for steels in all aspects of steel production, manufacturing and use, and, on the other hand, the advances in the field of stress tensor distribution monitoring targeting the prediction of crack initiation and propagation.

The balanced contribution of both industry and academia in this Special Issue aspires to offer a comprehensive overview and roadmap in this hot industrial field.

Prof. Evangelos Hristoforou
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 papers will be 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. Metals 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 1000 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

  • Non-destructive testing
  • Acoustic and ultrasonic methods
  • X-ray imaging techniques
  • Surface electromagnetic methods
  • Stress tensor distribution
  • Microstructure in steels and dislocation density
  • Steel industry and metallurgy
  • Magnetic properties of steels
  • Sensors for stress monitoring
  • Magnetic modeling in steels

Published Papers (2 papers)

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Research

Open AccessArticle RBF-Neural Network Applied to the Quality Classification of Tempered 100Cr6 Steel Cams by the Multi-Frequency Nondestructive Eddy Current Testing
Metals 2017, 7(10), 385; doi:10.3390/met7100385
Received: 18 August 2017 / Revised: 13 September 2017 / Accepted: 15 September 2017 / Published: 21 September 2017
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Abstract
This article proposes a Radial Basis Function Artificial Neural Network (RBF-ANN) to classify tempered steel cams as correctly or incorrectly treated pieces by using multi-frequency nondestructive eddy current testing. Impedances at five frequencies between 10 kHz and 300 kHz were employed to perform
[...] Read more.
This article proposes a Radial Basis Function Artificial Neural Network (RBF-ANN) to classify tempered steel cams as correctly or incorrectly treated pieces by using multi-frequency nondestructive eddy current testing. Impedances at five frequencies between 10 kHz and 300 kHz were employed to perform the binary sorting. The ANalysis Of VAriance (ANOVA) test was employed to check the significance of the differences between the impedance samples for the two classification groups. Afterwards, eleven classifiers were implemented and compared with one RBF-ANN classifier: ten linear discriminant analysis classifiers and one Euclidean distance classifier. When employing the proposed RBF-ANN, the best performance was achieved with a precision of 95% and an area under the Receiver Operating Characteristic (ROC) curve of 0.98. The obtained results suggest RBF-ANN classifiers processing multi-frequency impedance data could be employed to classify tempered steel DIN 100Cr6 cams with a better performance than other classical classifiers. Full article
(This article belongs to the Special Issue Advanced Non-Destructive Testing in Steels)
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Open AccessArticle Acoustic Emission Signatures of Fatigue Damage in Idealized Bevel Gear Spline for Localized Sensing
Metals 2017, 7(7), 242; doi:10.3390/met7070242
Received: 30 May 2017 / Revised: 26 June 2017 / Accepted: 26 June 2017 / Published: 30 June 2017
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Abstract
In many rotating machinery applications, such as helicopters, the splines of an externally-splined steel shaft that emerges from the gearbox engage with the reverse geometry of an internally splined driven shaft for the delivery of power. The splined section of the shaft is
[...] Read more.
In many rotating machinery applications, such as helicopters, the splines of an externally-splined steel shaft that emerges from the gearbox engage with the reverse geometry of an internally splined driven shaft for the delivery of power. The splined section of the shaft is a critical and non-redundant element which is prone to cracking due to complex loading conditions. Thus, early detection of flaws is required to prevent catastrophic failures. The acoustic emission (AE) method is a direct way of detecting such active flaws, but its application to detect flaws in a splined shaft in a gearbox is difficult due to the interference of background noise and uncertainty about the effects of the wave propagation path on the received AE signature. Here, to model how AE may detect fault propagation in a hollow cylindrical splined shaft, the splined section is essentially unrolled into a metal plate of the same thickness as the cylinder wall. Spline ridges are cut into this plate, a through-notch is cut perpendicular to the spline to model fatigue crack initiation, and tensile cyclic loading is applied parallel to the spline to propagate the crack. In this paper, the new piezoelectric sensor array is introduced with the purpose of placing them within the gearbox to minimize the wave propagation path. The fatigue crack growth of a notched and flattened gearbox spline component is monitored using a new piezoelectric sensor array and conventional sensors in a laboratory environment with the purpose of developing source models and testing the new sensor performance. The AE data is continuously collected together with strain gauges strategically positioned on the structure. A significant amount of continuous emission due to the plastic deformation accompanied with the crack growth is observed. The frequency spectra of continuous emissions and burst emissions are compared to understand the differences of plastic deformation and sudden crack jump. The correlation of the cumulative AE events at the notch tip and the strain data is used to predict crack growth. The performance of the new sensor array is compared with the conventional AE sensors in terms of signal to noise ratio and the ability to detect fatigue cracking. Full article
(This article belongs to the Special Issue Advanced Non-Destructive Testing in Steels)
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