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The quality of components under fused filament fabrication (FFF) is related to the correct filament spacing and bonding of successively deposited layers and is evaluated mainly by scanning electron microscopy (SEM). However, it is a destructive technique and real-time evaluation is not possible. Optical coherence tomography (OCT), on the other hand, is an optical method that acquires cross-sectional images non-invasively and in real-time. Therefore, this paper aims to propose and validate the use of OCT as a non-destructive quality evaluation tool for FFF using Polylactic Acid (PLA) filaments.

PLA three-dimensional (3D) printed samples were made in a variety of nozzle temperatures and mesh spacing. These samples were fractured in liquid nitrogen and inspected using SEM (as a gold standard) to evaluate dimensions and morphology, then the samples were evaluated by OCT in the same area, allowing the results confrontation.

Our results indicate a good correlation between OCT and SEM for the dimensional assessment of layers. When the filament was extruded in lower temperatures, the OCT images presented sharply defined interfaces between layers, in contrary to higher nozzle temperatures, denoting better fusion between them. However, higher extruding temperatures are incurred in greater deviations from nominal dimensions of the mesh. Finally, we demonstrate the advantage of a full 3D tomographic reconstruction to inspect within a FFF sample, which enabled the inspection of “hidden” information, not visible on a single cross-sectional cut.

This paper proposes OCT as a novel and nondestructive evaluation tool for FFF.

Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.

The purpose of this paper is to present and implement an algorithm to prioritize welding quality deterioration factors for improving welding personnel performance. A case study is carried out in a piping components fabrication process which supplies these components to oil and gas production and processing facilities on the Norwegian continental shelf (NCS).

The quality deterioration factors' prioritization is carried out using statistical methods in conjunction with the data recorded in the welding inspection database (WIDB) of the case study company. Data cleaning and rearrangements were performed to reflect final objective. Based on the welding procedure specifications (WPSs) and quality imperfection groups classified in NS‐EN ISO 6520‐1, the analysis is performed to prioritize the welding quality deterioration factors.

Based on the WPSs and quality imperfection groups classified in NS‐EN ISO 6520‐1, it is possible to prioritize the welding quality deterioration factors. These factors are possible to use for improving the performance of welding personnel to assure the quality of welds in steel fabrications.

The factors prioritized are possible to use for improving the performance of welding personnel to assure the quality and reliability of welds in a steel fabrication.

Assuring quality as proposed in the manuscript, the catastrophic failures that are potential in production and process plants can be mitigated. This enhances health, safety and environmental performance of welds in steel fabrications.

The value of this paper is to illustrate an innovative approach to a real life quality problem; it demonstrates how the application of qualitative and quantitative quality instruments in accordance with technical specification can help in increasing and maintaining product compliance and in optimizing the management of resources.

Experimental research was conducted on the effects of surface roughness on ultrasonic non-destructive testing of electron beam melted (EBM) additively manufactured Ti-6Al-4V. Additive manufacturing (AM) is a developing technology with many potential benefits, but certain challenges posed by its use require further research before AM parts are viable for widespread use in the aviation industry. Possible applications of this new technology include aircraft battle damage repair (ABDR), small batch manufacturing to fill supply gaps and replacement for obsolete parts. This paper aims to assess the effectiveness of ultrasonic inspection in detecting manufactured flaws in EBM-manufactured Ti-6Al-4V. Additively manufactured EBM products have a high surface roughness in “as-manufactured” condition which is an artifact of the manufacturing process. The surface roughness is known to affect the results of ultrasonic inspections. Experimental data from this research demonstrate the ability of ultrasonic inspections to identify imbedded flaws as small as 0.51 mm at frequencies of 2.25, 5 and 10 MHz through a machined surface. Detection of flaws in higher surface roughness samples was increased at a frequency of 10 MHz opposed to both lower frequencies tested.

The approach is to incorporate ultrasonic waves to identify flaws in an additive manufactured specimen

A wave frequency of 10 MHz gave good results in finding flaws even with surface roughness present.

To the best of the authors’ knowledge, this was the first attempt that was able to identify small flaws using ultrasonic sound waves in which surface roughness was present.

The purpose of this paper is to present the results of acoustic emission (AE) and ultrasonic inspection of two H₂S storage tanks carried out in a heavy water plant, in order to characterize point type defects observed during earlier ultrasonic inspection and to ensure that these defects are not growing during hydrotesting of the tanks.

Using multiple AE sensors and AE source location methodology, the entire tank could be covered to detect and locate any dynamic sources of AE associated with local plastic deformation and/or growing discontinuities from any part of the tank during the hydrotest. For confirmation of the results obtained by AE, ultrasonic inspection on the tanks and on virgin plates from which the tanks were manufactured, were carried out.

The AE signals generated during first pressurisation are attributed to the micro yielding of the material of the tanks. A few scattered AE events were observed at a few locations during the hydrotesting of the tanks and these are due to structural and rubbing noise. During hold periods and repressurising cycle of the hydrotesting, no detectable AE events were observed and this confirmed the absence of any growing discontinuity in the tanks during the hydrotesting. Ultrasonic inspection on the tanks and on virgin plates confirmed that the point type defects detected are manufacturing defects and not formed during service life.

The combined results from AE and ultrasonic techniques confirmed the structural integrity of the tanks and ensured their healthiness for continued operation.

The paper brings out the use of AE and ultrasonic techniques for monitoring hydrotesting of storage tanks of a heavy water plant. The storage tanks where point type defect indications were reported during previous ultrasonic inspection and whether these defects are growing during hydrotesting of the tanks or not, were required to be known before the tanks are put in to further service. AE signals collected during pressurising and repressurising cycles of the hydrotest and subsequent inspection by ultrasonic confirmed the vessels to be free from growing defects during the hydrotest and provided baseline data for future inspection.

AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity.

The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity.

The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm³ and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm³. The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains.

The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications.

To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.

This paper aims to presents a novel application of neural network (NN) pattern recognition to ore rock sorting using inductive electromagnetic (EM) sensors.

The impedance of a metallic rock can be measured with an inductive method based on Faraday’s law and eddy current theory. A virtual rock model is then created for the simulation of the EM measurements. An NN is trained to differentiate between waste and useful ore samples (containing high amount of minerals) based on the EM sensor signals produced by the rocks.

The NN solution showed high accuracy of rock classification and produced relatively robust results from signals with noise.

A pattern recognition NN was applied to classify low- and high-grade ore samples. It has the potential to determine the approximate amount of conductive materials inside ore rocks through multiple classes. This method can be used to improve the performance of EM-based ore sorting for mineral pre-concentration.

In this study, the effect of Friction Stir Welding on a 6061 aluminium alloy reinforced with 20% of alumina particles metal matrix composite was analysed. The sheets were joined by employing a tool rotating speed of 700 RPM and a welding speed of 250 mm/min. The optical and scanning electron microscopy observations performed on the different zones of FSW joints cross section revealed the different structures of the nugget, the thermo‐mechanical affected zone and the heat affected zones thanks to the difference in reinforcing particles dimensions as a consequence of friction process. After FSW the material was aged in a 3.5% NaCl solution for 1, 10 and 90 days. The aim of this work is to apply thermoelastic stress analysis to the study of crack formation and propagation of friction stir welded MMC sheets, during cyclic fatigue tests. Fatigue tests were carried out under the axial total stress‐amplitude control mode with R=omin/omax = 0.1 using a resonant electro‐mechanical testing machine (TESTRONICTM 50 25 KN by RUMUL (SUI)). All the mechanical tests were performed on as‐FSW and aged samples up to failure. The TSA measurement system allowed the crack evolution to be observed in real‐time during fatigue cycles and stress fields to be derived on the specimens from the temperature variation measured.