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Advanced Manufacturing and Nondestructive Testing Techniques
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Advanced Manufacturing and Nondestructive Testing Techniques

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Additive Manufacturing Technologies".

Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 7411

Special Issue Editor

Dr. Anne Francoise Obaton

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Guest Editor
French National Metrology Institute (NMI), Laboratoire National de Métrologie et d’Essais (LNE), 75015 Paris, France
Interests: additive manufacturing; nondestructive testing; metrology

Special Issue Information

Dear Colleagues,

Metallic additive manufacturing (AM) is gaining in popularity in the industry (aerospace, defense, nuclear, etc.) and in the medical sector, particularly the powder bed fusion (PBF) and the directed energy deposition (DED) categories of processes. In such critical sectors, the integrity and geometrical conformity to the numerical design of the manufactured parts need to be demonstrated.

First, to limit the defects and deviations from the numerical design, the implementation of digital twins of the AM process chain is a solution. Second, to limit the defects during the AM process, the monitoring of the process, coupled with a feedback loop, involving artificial intelligence (AI), is a solution. Third, to detect defects and deviations in the post-process AM parts, non-destructive testing (NDT) and metrology are required. However, the complexity in shape enabled by AM, including internal structures and the rough surface finish, poses a challenge to the quality control of AM parts. X-ray Computed Tomography (XCT), implementing cone beam or synchrotron radiation, is the most performing NDT method as it enables NDT and metrology. However, linear and nonlinear Resonant Ultrasound Spectroscopy (RUS), swept sine methods and Impulse Excitation Method (IEM), are alternative very performing methods for NDT. Moreover, considering dense and large DED parts, tomosynthesis and Phased Array Ultrasonic Testing (PAUT) are more adapted. In addition, the potential for inspection of these NDT methods can be increased by the implementation of IA, allowing the operator's influence to be limited.

Dr. Anne Francoise Obaton
Guest Editor

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Keywords

  • additive manufacturing (AM)
  • powder bed fusion (PBF)
  • directed energy deposition (DED)
  • process monitoring
  • digital twins
  • nondestructive testing (NDT)
  • metrology
  • artificial intelligence (AI)
  • X-ray computed tomography (XCT)
  • synchrotron
  • tomosynthesis
  • resonant ultrasound spectroscopy (RUS)
  • swept sine methods
  • impulse excitation method (IEM)
  • nonlinear RUS
  • phased array ultrasonic testing (PAUT)

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Published Papers (4 papers)

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Research

13 pages, 12308 KiB  
Article
Application of Convolutional Neural Networks for Classifying Penetration Conditions in GMAW Processes Using STFT of Welding Data
by Dong-Yoon Kim, Hyung Won Lee, Jiyoung Yu and Jong-Kyu Park
Appl. Sci. 2024, 14(11), 4883; https://doi.org/10.3390/app14114883 - 4 Jun 2024
Cited by 6 | Viewed by 1361
Abstract
For manufacturing components with thick plates, such as in the heavy equipment and shipbuilding industries, the gas metal arc welding (GMAW) process is applied. Among the components that apply the thick plate GMAW process, there are groove butt joints, which are fabricated through [...] Read more.
For manufacturing components with thick plates, such as in the heavy equipment and shipbuilding industries, the gas metal arc welding (GMAW) process is applied. Among the components that apply the thick plate GMAW process, there are groove butt joints, which are fabricated through multi-pass welding. Various welding qualities are managed in multi-pass welding, and the root-pass weld is controlled to ensure complete joint penetration (CJP). Currently, the state of complete joint penetration during root-pass welding is managed visually, making it difficult to confirm the penetration condition in real time. Therefore, there is a need to predict the penetration condition in real time. In this study, we propose a convolutional neural network (CNN)-based prediction model that can classify penetration conditions using welding current and voltage data from the root pass of V-groove butt joints. The root gap of the joints was varied between 1.0 and 2.0 mm, and the wire feed rate was adjusted. During welding, the current and voltage were measured. The welding current and voltage are transformed into a short-time Fourier transform (STFT) representation depicting the arc and wire extension lengths. The transformed dynamic resistance STFT information serves as the input variable for the CNN model. Preprocessing steps, including thresholding, are applied to optimize the input variables. The CNN architecture comprises three convolutional layers and two pooling layers. The model classifies penetration conditions as partial joint penetration (PJP), CJP, and burn-through, achieving a high accuracy of 97.8%. The proposed method facilitates the non-destructive evaluation of the root-pass welding quality without expensive monitoring equipment, such as vision cameras. It is expected to be immediately applied to the thick plate welding process using readily available welding data. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Nondestructive Testing Techniques)
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12 pages, 2423 KiB  
Article
Automated Quantification of Raster Orientation of Fused Filament Fabrication Components Using Ultrasonic Testing
by Atik Amin, David A. Jack and Trevor J. Fleck
Appl. Sci. 2024, 14(11), 4769; https://doi.org/10.3390/app14114769 - 31 May 2024
Cited by 1 | Viewed by 1058
Abstract
An automated method for nondestructively characterizing the layer-by-layer raster orientation of additively manufactured components fabricated via the fused filament fabrication (FFF) process is presented, which utilizes full waveform capture of the ultrasonic signal paired with two-dimensional fast Fourier transform analysis. The proposed method [...] Read more.
An automated method for nondestructively characterizing the layer-by-layer raster orientation of additively manufactured components fabricated via the fused filament fabrication (FFF) process is presented, which utilizes full waveform capture of the ultrasonic signal paired with two-dimensional fast Fourier transform analysis. The proposed method extracts internal features of the fabricated component at various depths and then applies the two-dimensional Fourier transformation in the spatial domain to analyze the raster path and extract the orientation. Three material systems are studied: a standard polymer (Poly cyclohexylenedimethylene terephthalate glycol, PCTG), an engineered polymer (high-temperature nylon, HTN) and a carbon fiber-filled polymer (polyethylene terephthalate, PET-CF). Samples were fabricated using an industrial-grade FFF system and scanned using a high-resolution custom immersion ultrasonic platform. Studies were performed using both a 10 MHz and a 15 MHz spherically focused transducer, with the 10 MHz transducer yielding more accurate and more consistent results for the investigated material systems. The analyzed results show that the presented automated method can accurately identify the direction of the raster path with an error within 1°–2° in each of the first 9~10 deposited layers of the investigated PCTG and the PET-CF samples, and the first 14 layers of the HTN samples. This study provides an approach for the automated analysis of the internal features of FFF components using ultrasonic testing, which can further inform the quality control process, in turn increasing reliability and enabling acceptance of AM parts in various industries. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Nondestructive Testing Techniques)
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15 pages, 8526 KiB  
Communication
Controlled Creation of Contact Cracks in Additive Manufactured Components
by Daniel Preston, Ahmed Ashour, Julian Wright, James Watts, Daniel Sanmartin and Jacques Wood
Appl. Sci. 2023, 13(21), 11990; https://doi.org/10.3390/app132111990 - 2 Nov 2023
Viewed by 2086
Abstract
Techniques for the controlled seeding and growth of cracks are urgently required for non-destructive testing technique evaluation, particularly for additive manufactured (AM) samples. This paper describes a method that uses a combination of the tensile load and the resonance excitation of notched AM [...] Read more.
Techniques for the controlled seeding and growth of cracks are urgently required for non-destructive testing technique evaluation, particularly for additive manufactured (AM) samples. This paper describes a method that uses a combination of the tensile load and the resonance excitation of notched AM samples, with in situ monitoring of the resonance frequency serving to track the crack dimensions. Mechanical low-cycle fatigue cracks, ranging in length from ~0.3 mm to ~5 mm, were successfully created in five AM samples using this technique. The samples were non-destructively characterized using optical microscopy and Nonlinear Resonance (NLR) testing. The exploitation of resonance enabled the concentration of a significant number of stress cycles on the samples in much shorter timespans than conventional fatigue testing, enabling a high throughput while utilizing compact components. Furthermore, the tracking of the resonance frequency shift throughout the process enabled non-invasive and no-contact real-time condition monitoring. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Nondestructive Testing Techniques)
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18 pages, 7111 KiB  
Article
In Vivo Bone Progression in and around Lattice Implants Additively Manufactured with a New Titanium Alloy
by Anne-Françoise Obaton, Jacques Fain, Dietmar Meinel, Athanasios Tsamos, Fabien Léonard, Benoît Lécuelle and Madjid Djemaï
Appl. Sci. 2023, 13(12), 7282; https://doi.org/10.3390/app13127282 - 19 Jun 2023
Cited by 8 | Viewed by 1803
Abstract
The osseointegration in/around additively manufactured (AM) lattice structures of a new titanium alloy, Ti–19Nb–14Zr, was evaluated. Different lattices with increasingly high sidewalls gradually closing them were manufactured and implanted in sheep. After removal, the bone–interface implant (BII) and bone–implant contact (BIC) were studied [...] Read more.
The osseointegration in/around additively manufactured (AM) lattice structures of a new titanium alloy, Ti–19Nb–14Zr, was evaluated. Different lattices with increasingly high sidewalls gradually closing them were manufactured and implanted in sheep. After removal, the bone–interface implant (BII) and bone–implant contact (BIC) were studied from 3D X-ray computed tomography images. Measured BII of less than 10 µm and BIC of 95% are evidence of excellent osseointegration. Since AM naturally leads to a high-roughness surface finish, the wettability of the implant is increased. The new alloy possesses an increased affinity to the bone. The lattice provides crevices in which the biological tissue can jump in and cling. The combination of these factors is pushing ossification beyond its natural limits. Therefore, the quality and speed of the ossification and osseointegration in/around these Ti–19Nb–14Zr laterally closed lattice implants open the possibility of bone spline key of prostheses. This enables the stabilization of the implant into the bone while keeping the possibility of punctual hooks allowing the implant to be removed more easily if required. Thus, this new titanium alloy and such laterally closed lattice structures are appropriate candidates to be implemented in a new generation of implants. Full article
(This article belongs to the Special Issue Advanced Manufacturing and Nondestructive Testing Techniques)
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