Artificial Defects Research Articles

Influence of Artificial and Process-induced Defects on Very High Cycle Fatigue Characteristics of 316L Stainless Steel Produced by Laser Powder Bed Fusion

Influence of Artificial and Process-induced Defects on Very High Cycle Fatigue Characteristics of 316L Stainless Steel Produced by Laser Powder Bed Fusion

Time‐domain spectra of ultrasonic wave transmitted through granite and gypsum samples containing artificial defects

AbstractThe internal defects in rock masses can significantly impact the quality and safety of geotechnical projects. Mechanical waves, as a common nondestructive testing (NDT) method, can reflect the external and internal structures of rock or rock masses. Analyses on the reflected and transmitted waves enable nondestructive identification and assessment of potential defects within rocks. Previous studies mainly focused on the variation of single or limited wave features like main frequency, amplitude and energy between the intact and non‐intact samples. In fact, most information contained in the waveforms is neglected. Techniques of data mining can provide a powerful tool to reveal this information and therefore a more accurate determination of the internal structures. In this study, 995,412 NDT data from 14 types of granite and gypsum samples with different cross‐section shapes and different types of defects are recorded by an ultrasonic wave generation and collection system. This dataset can be used not only as the training data for defect classification in NDT but also as a good reference for conventional NDT analyses. Besides, time‐series data analysis is an opportunity and challenging issue, this dataset holds great potential for broader application in general time‐series classification analysis.

Quantitative characterisation of debris cloud-induced pitting damage in AL-Whipple shields of spacecraft using infrared thermography

ABSTRACT Quantitatively evaluating the debris cloud-induced pitting damage in the AL-Whipple structure of spacecraft, is still a challenge, let alone microstructural plastic deformation usually accompanied by geometric cratering defects, which may decrease material stress-carrying capacity and structural life. With this motivation, a dedicated modelling technique is proposed to gain an insight into various modalities (i.e., geometric and plastic defects) of pitting damage-induced thermal modulation for infrared inspection. Then, a sequence of infrared images is obtained and analysed using the gap smoothing algorithm and naive Bayes classification methods. On this basis, a quantitative characterization framework is proposed to characterize the damage modalities and their morphologies. This method is validated using a 3D numerical model which contains pitting damage acquired from the real-world structure. Modality classification and morphology evaluation results are well consistent with the artificial defects, in terms of the modality, diameter, and depth. Then they‘re experimentally corroborated, in which the severity and distribution of pitting damage are precisely characterized, in terms of the crater number and morphologies, as well as the size of plastic regions around these craters, which are consistent with the actual observations. This work proposes a non-destructive strategy for pitting damage evaluation, which is engineering-friendly.

Fatigue Crack Segmentation and Characterization of Additively Manufactured Ti‐6Al‐4V Using X‐Ray Computed Tomography

ABSTRACTX‐ray computed tomography (XCT) is extremely useful for the non‐destructive analysis of additively manufactured (AM) components. AM components often show manufacturing defects such as lack‐of‐fusion (LoF), which are detrimental to the fatigue life of components. To better understand how cracks initiate and propagate from internal defects, we fabricated Ti‐6Al‐4V samples with an internal cavity using electron beam powder bed fusion. The samples were tested in high‐cycle and very high‐cycle fatigue regimes. XCT was used to locate crack initiation sites and to determine characteristic properties of cracks and defects with the aid of deep learning segmentation. LoF defects exposed to the outer surface of the samples after machining were found to be as detrimental to fatigue life as the internal artificial defects. This work can benefit industries that utilize the AM of high‐strength, lightweight alloys, in the design and manufacturing of components to improve part reliability and fatigue life.

One-dimensional N-layer thermal modelling for effective machine learning training data generation for nondestructive testing of composite parts with infrared thermography

The analytical solution of the one-dimensional N-layer thermal model (NLM) was successfully employed to generate training data for a machine learning (ML) based procedure for the nondestructive inspection of carbon fiber reinforced composite parts with infrared thermography (IRT). The main objective was to identify a reliable correlation between the experimental data of a pulsed IRT experiment and the NLM prediction, thereby enabling the use of simulated data for ML training. This paper focuses on the initial stages of this process, in more detail on the analytical modelling and experimental data preprocessing, such as normalization, correction of experimental shortcomings and feature selection for machine learning. For pulse phase thermography (PPT) simulated and experimentally derived phase data was compared directly in the frequency domain. Therefore, the features for training and validation of ML were defined from those phase spectra in frequency domain. The suitability of these features for automated and reliable defect depth and/or defect material detection was investigated in both simulated and measured IRT test data. As a basis for feature selection, we used referenced and normalized phase-frequency curves as a function of defect depth. A correlation was identified between the results of the experimental and the simulated feature sets, both qualitatively and quantitatively. To demonstrate the practical applicability of this method, two different, generic ML techniques, multilayer perceptron and random forest regression, were tested as examples. The investigation was performed on plates made of multidirectional carbon fiber reinforced polymer (CFRP) with artificial defects made from three different materials.

A Comparative Study of Non-Destructive Testing Techniques: Active Thermography versus Shearography for 3D-Printed Thermoplastic Composites Reinforced with Continuous Carbon Fiber

This study investigates the feasibility and effectiveness of two non-destructive testing methods, active thermography and shearography, on 3D-printed thermoplastic (TP) composites reinforced with continuous carbon fiber. Artificial defects were introduced into the composite plate to benchmark the detection capabilities of these non-destructive testing techniques (NDT). Active thermography produced a thermogram that highlighted defects through variations in surface temperature. Although effective for identifying defects ranging from 3 to 10 mm in size at four different depths, specifically 1 mm, 1.25 mm, 1.5 mm, and 1.75 mm, through the thickness of a 2.8 mm plate, the method encountered some limitations. It faced challenges in detecting deeper defects and accurately determining their shapes. Shearography, which utilizes fringe pattern distortions to detect surface displacement anomalies, successfully identified near-surface defects within the same size range. However, it required more expertise for accurate interpretation and struggled with detecting smaller and deeper defects. The complementary strengths and limitations of these methods suggest that employing both could offer a more comprehensive solution for defect detection in 3D-printed TP composites.

Effect of Internal Technological Defects and Loading Waveform on Structural Composite Fatigue Life

This paper explores the effect of internal technological defects on the fatigue life of carbon fiber reinforced plastic (CFRP) under simple loading waveforms. One conducted experiments on CFRP specimens with embedded artificial defects, including delamination’s (circular dry-spot) and wrinkling. Following quasi-static tests, one developed a fatigue testing program using triangular and sine waveforms. The findings indicate that these simple waveforms do not significantly affect the fatigue resistance of defect-free CFRP or CFRP with internal technological defects. The presence of the wrinkling defect significantly diminishes the fatigue resistance of CFRP. However, constructing fatigue life curves in relative terms reveals that the reduction in fatigue properties is directly related to a decrease in strength properties. In the case of delamination (dry-spot) defects, a reduction in fatigue life by approximately 2.5 times was observed across the tested range.

Thermal testing of corundum ceramics: pulse heating and optimized data processing algorithms

A review of the types of defects in the production of corundum ceramic tiles and traditional methods of integrity testing of products made of this material is carried out. The integrity of tiles containing artificial defects was investigated by the method of active thermal testing using pulsed optical heating. A scheme of two-way thermal testing with software processing of the initial thermograms using the Parker method is applied. It has been established that the best results in detecting internal defects in ceramic tiles with a thickness of 10 mm during thermal stimulation using xenon lamps are obtained by the method of two-way thermal testing with the construction of thermal conductivity maps.

Effect of hydrocortisone on the course of leprosic infection in an experiment

Leprosy is a chronic infectious disease caused by Mycobacterium leprae (M. leprae), accompanied by a violation of various parts of the immune system. In this regard, the development of an experimental model of leprosy may be based on the creation of an artificial defect of the macrophage link, in particular, using glucocorticoids. Aim: To develop and characterize a model of generalized leprosy infection in mice against the background of prolonged administration of hydrocortisone.Material and methods. To conduct the study, the following groups were formed: the control group – 50 mice infected by the Shepard method; the experimental group – 50 mice similarly infected against the background of hydrocortisone administration. Hydrocortisone was administered intramuscularly in a single daily dose of 25 mg/kg on the first day 2 hours before infection with M. leprae suspension, then in the form of four courses of 2 weeks at two-week intervals.Results. The administration of hydrocortisone led to an acceleration of the reproduction of mycobacteria in the paw pad compared with the control (without hydrocortisone). The generalization of leprosy infection is confirmed by the presence of lepromatous structures in the form of granulomas from macrophages with a high content of M. leprae in liver, spleen, lungs and kidneys.Conclusion. This model allows us to study the properties of the pathogen and the mechanisms of pathogenesis of the disease, and to screen pharmacological drugs with potential anti-leprosy activity in the future.

Monitoring of a fatigue test on a full-size railway steel bridge with acoustic emission

Within the frame of the Rail4Future project (https://www.rail4future.com) co-funded by the Austrian Research Promotion Agency, a railway steel bridge was removed from the track and transported to a workshop in one piece. Artificial defects were introduced at different sections of the bridge structure as per advice of the scientific project partners in order to initiate fatigue cracks in the course of the subsequent fatigue test. One objective of the fatigue test was to identify suitable measurement and testing techniques for monitoring of fatigue crack growth. Among other techniques utilised by different project partners, acoustic emission monitoring was applied by TÜV AUSTRIA. This contribution describes the preparation as well as the performance of the acoustic emission monitoring and the results obtained. It is shown that this non-destructive testing technique is suitable for fatigue crack monitoring.

Optimization of thermal shock response spectrum as infrared thermography post-processing methodology using Latin hypercube sampling and analytical thermal N-layer model

In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data post-processing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-to-noise ratio (SNR) were used.

Induction Motor Tacholess Fault Detection in Transient Speeds Using Adaptive Generalized Vold Kalman Filter

Fault detection are essential in machine condition monitoring to ensure reliable and efficient operations. This article introduces a methodology to achieve this goal, using tacholess method. Techniques such as the Hilbert transform, adaptive observer, generalized demodulation transform (GDT), and Vold-Kalman filtering (VKF) are combined. The novelty introduced in this article lies in the use of the adaptive observer and the incorporation of the estimated speed to make the generalized Vold-Kalman filter adaptive as a function of motor current. Experimental evaluations were carried out, simulating artificial defect, corresponds to a scenario where the rotor has multiple impacts per revolution, often associated with issues such as misalignment or imbalance. These experimental findings demonstrate the performance of the proposed method, with an adaptive filter showing improved signal extraction across a range of operating situations. A diagnostic protocol has been developed, using statistical analysis, to guarantee the applicability of the method under all operating conditions.

Non-local fatigue criterion based on standard deviation for notches and defects in Ti-6Al-4V

The aim of this study is to propose a non local multiaxial fatigue criterion in order to assess the fatigue limit of stress concentrator such as notches or defects in Ti-6Al-4V. This criterion is based on the standard deviation of the stresses integrated over a given volume around the hot spot point. Up to now, the standard deviation has never been tested as an indicator of stress heterogeneity to assess fatigue life of notch components. Four parameters are necessary to identify the criterion. The criterion is first validated on notched samples based on literature results and shows very good results. A set of experimental tests is conducted on natural and artificial defects including clustering defects and demonstrates the flexibility and accuracy of the criterion.

Laser fiber cleave: Modeling and fundamentals

In the laser-assisted fiber cleaving process, an ultrashort, high-energy laser is used to introduce a flaw on the optical fiber surface, and then the fiber is cleaved under a tension or bending load. The quality of the cleaved end depends on the crack propagation from the artificial flaw. To understand the cleaving process and the effects of flaw parameters on cleaving quality, crack propagation is modeled by considering a pre-existing surface flaw using a graph-based finite element method (GraFEA). GraFEA is based on the nonlocal multiple cracking simulation framework for brittle and quasi-brittle materials. First, a three-point bending test is conducted to calibrate material parameters in GraFEA for commercial fused silica glass. Subsequently, the model is validated by four-point bending and ring-on-ring tests. After validation, the fiber cleaving process is investigated by parametric simulations in which different loading types (bending or tension) are considered. Finally, a beneficial process window is obtained and recommended for improved cleaving quality.

Efficient Electron Injection into Graphullerene Enables Reversible NaC2 Sodium Storage.

Sodium-ion batteries are emerging as promising alternatives to conventional lithium-based technology, offering solutions to challenges in large-scale grid storage. However, the capacity of conventional graphite-based anodes for storing Na-ions is inherently limited by suboptimal thermodynamic interactions and irreversible structural changes that occur in the anode during charge-discharge cycles. Herein, we present a computational design that explores the potential of graphullerene, a two-dimensional framework with interconnected fullerene moieties, for the reversible storage of Na-ions. A unique aspect of this design is the electron injection capacity into the graphullerene anode, reaching 15 electrons per fullerene moiety, which is the highest limit to date. This advancement enables large-scale Na-ion storage up to the stoichiometry of NaC2, exhibiting specific capacity of 551 mAhg-1 and averaged open circuit voltage of 0.18 V vs Na/Na+. In addition, the multilayered arrangement of stored Na-ions enhances the Na-ion diffusivity on the graphullerene surface, leading to rapid insertion and extraction kinetics. Thus, raising the electron injection limit offers a promising strategy to transform carbon-based anodes into suitable candidates for reversible Na-ion storage, without relying on artificial defect introduction or doping.

Comparison of remineralizing efficacy of two novel preparation of arginine and theobromine on white spot lesion - an in vitro SEM analysis

ABSTRACT Purpose Evaluation of the efficacy of two novel formulations of arginine and theobromine in remineralizing artificial enamel defects using in vitro testing techniques- Microhardness and SEM Materials and methods Enamel slabs measuring 3 mm by 3 mm by 1.5 mm were created using 11 healthy premolars that were excised for orthodontic treatment. Group 1 (control, treated with merely a remineralization solution), Group 2 (0.05% arginine in 10 ml distilled water), and Group 3 (2% theobromine in 10 ml distilled water) were the three groups to which the slabs were randomly allocated (n = 7 per group). The pH cycle was run for 14 days, starting with a 72-h demineralization period and ending with 11 days of remineralization in the corresponding solutions. Prior to and during remineralization, microhardness was measured, and surface features were assessed using scanning electron microscopy (SEM). Statistical analysis, including Paired T Test, one-way ANOVA, and post hoc Tukey tests, determined that the mean values were significant at the 0.05 level. Results The control group showed a mean microhardness increase from 192.09 ± 19.30 to 208.58 ± 5.76. The arginine group increased significantly from 217.20 ± 4.24 to 304.00 ± 15.96, and the theobromine group from 214.09 ± 7.67 to 283.14 ± 10.91. Statistical analysis indicated that both arginine and theobromine groups had significantly greater microhardness improvements compared to the control (p < 0.05). SEM revealed that although arginine and theobromine enhanced enamel microhardness, surface morphology remained irregular and non-uniform. Conclusion Arginine and theobromine improve enamel microhardness and promote remineralization, but they do not create a uniform surface texture. Further research is needed to optimize their use and explore long-term effects on enamel surface uniformity.

Application of optical fiber distributed acoustic sensing system in ultrasonic detection

Abstract The optical fiber distributed acoustic sensing system can continuously detect acoustic signals along the optical fiber, with the characteristics of wide response frequency band, large capacity, anti-electromagnetic interference and so on. An ultra-sensitive distributed optical fiber acoustic sensing system (uDAS) was produced with a high response frequency up to 100 kHz and thousands of sensing points. The arrayed ultrasonic detection can be realized by the distributed characteristics of the system. For verifying its ultrasonic detection ability, it is applied to ultrasonic nondestructive testing of structural defects. 20 optical fiber ultrasonic detection units were constructed. Each one is a fiber ring with a diameter of about 1.5 cm and a length of 2 meters. Two cubic cement structures with a side length of 30 cm were manufactured for experiment, one of which had an artificial internal defect. The optical fiber array was attached to the surface of the cement structure, and the ultrasonic signal with a frequency of 40 kHz was excited at a single point on the opposite side. The ultrasonic propagation speed changes on account of the defects such as holes and loose texture in the propagation path. The defects in the structure are identified and located by analyzing the difference of arrival time of waves. It is expected that such sensing system could found important applications in structure defect detection.

Expanded vermiculite acting as artificial flaws to enhance the tensile properties of high-strength engineered cementitious composites

In this study, an expanded vermiculite (EV) was proposed to substitute silica sand, and act as artificial flaws in high-strength engineered cementitious composites (ECC) to improve its ductility. Experimental and analytical investigations were conducted on the mechanical behavior, micromechanical analysis and microstructural analysis of ECC from macro-, meso-, and micro-scales. The results demonstrated that the inclusion of EV reduced the compressive strength of ECC composites from 77.6 MPa to 60.2 MPa, although this does not hinder its role as a constituent of high-strength concrete (60 MPa). However, compared with that of 2.56 % in reference ECC mixture, due to the EV addings, the strain capacity of ECC mixtures was increased up to 8.22 %. On the other hand, incorporating fine EV particles (0.15 mm, 0.42 mm, and 2 mm) was positive to the tensile strength of ECC mixtures with a 23 % improvement, while the addition of coarser EV (1–3 mm and 3–5 mm) led to an 8 % reduction. At microscale, the incorporation of EV reduced the critical flaw size and increased the flaw size, significantly increased the number of active flaws, as a result, more micro-cracks were initiated, and thus greatly improving ductility of ECC.

Thermal testing of corundum ceramics: Classical techniques for optical heating

The types of defects and ways of their formation in the production of tiles from corundum ceramics are considered. The integrity of tiles containing artificial defects has been investigated by the method of active thermal non-destructive testing using optical heating. Single- and double-sided thermal control schemes are applied, as well as various methods of software processing of thermograms. It has been established that the best results in detecting internal defects in ceramic tiles with a thickness of 10 mm during thermal stimulation using halogen lamps are obtained by the method of unilateral thermal testing.

Dynamic Electric Discharge Paths in Liquid Metal Marble Arrays.

Electric discharge occurs ubiquitously in both natural and engineered systems, where the discharge paths provide critical information. However, control and visualization of discharge patterns is a challenging task. Here arrays of liquid metal marbles, droplets of a gallium-indium eutectic alloy with a copper-doped ZnS luminescent coating, are designed for pixelated visualization of electric discharge paths at optical imaging length-scales. The ZnS particles embed themselves into the surface of liquid metal droplets and are anchored by a self-limiting gallium oxide layer. The operation is achieved by generating spark discharges at inter-marble air gaps and reduced voltage drop across highly conducting liquid metal droplets. By taking advantage of the malleability of soft liquid metal marbles, the dynamic visualization platforms allow the manipulation of discharge path selections in configurable marble arrays and the embedding of artificial defect features. The systems are further integrated for characterizing dynamic changes in granular and soft systems, and for enabling logic computing and information encoded display. This demonstration holds promises for creating new-generation electric discharge-based optoelectronics.