Search

Modern techniques such as laser-based additive manufacturing (LBAM) technologies are being used nowadays in many industries for the fabrication of components of complicated geometries in smaller lead times. To compete with the conventional fabrication route, additive manufacturing (AM) technology must progress with appropriate NDE methods to ensure defect-free components. The common defects noticed in the AM components are gas porosities, clusters of porosities, micro-cracks, balling, lack of fusion and layer delamination among others. This paper aims to inspect subsurface defects in AM products which are one of the more challenging issues in AM. The surface finish of the AM components in as-printed condition is not amenable for the detection of subsurface defects for NDE using contact methods, e.g. ultrasonic techniques. Laser shearography and pulsed thermography by thermal excitation are extensively used as noncontact NDT techniques for the detection of subsurface defects in composite materials. Noncontacting NDT techniques may play a significant role in the in-process inspection of AM components, especially subsurface defects. This paper presents the capability, advantages and limitations of pulsed thermography and laser shearography techniques for the assessment of engineered defects in aluminum alloy coupon made through the laser powder bed fusion (LPBF) AM process. [ABSTRACT FROM AUTHOR]

Rayleigh waves are highly sensitive to surface and subsurface defects. However, the varying surface profiles of complex structures induce constant changes in the Rayleigh waveform. Furthermore, Rayleigh waves encompass multi-dimensional characteristics of subsurface defects, significantly increasing the difficulty of quantitatively detecting the sizes of subsurface defects in different dimensions. This paper presents a machine learning (ML)-based method for quantitatively detecting subsurface defect depth and width in curved structures using laser-generated Rayleigh waves. An experimentally validated finite element model is first established to construct a dataset of Rayleigh wave signals labeled with multidimensional subsurface defect sizes. The dataset is augmented from 188 to 368 samples using the Mix-up algorithm, with additional experimental signals incorporated. Feature parameters of Rayleigh waves are extracted in both time and frequency domains through waveform analysis and sliding window wavelet transform (SWT). These features are used to train ML models: Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Comparative results demonstrate GP achieves the highest prediction accuracy for subsurface defect depth and width on the test set, reaching 98.64% and 97.73%, respectively. The study highlights the integration of experimental and simulated data in training, which reduces costs while enhancing model robustness in practical applications. [ABSTRACT FROM AUTHOR]

In this paper, the effect of the stress relief annealing rate on residual stress was investigated using molecular dynamics simulation, and the difference in mechanical properties of workpieces before and after stress relief annealing was analyzed using nanoindentation simulation. The results show that the Stacking faults and dislocation motions are blocked during the heating stage to shift the stresses, and the residual stresses are released by the dislocation reaction dominated by Shockley dislocations. In the cooling stage, the Stair-rod dislocation reaction forms the Stacking Fault Tetrahedra to release the residual stress. The dislocation density is directly proportional to the residual stress during the stress relief annealing process, and the decrease in dislocation density leads to a decrease in surface hardness. [ABSTRACT FROM AUTHOR]

It is necessary to detect subsurface defects for a key metallic structural component especially a multilayer coating to ensure its structural integrity. In this paper, an imaging algorithm using the synthetic aperture focusing technique (SAFT) is developed for processing surface wave signals of array pickup electromagnetic acoustic transducer (EMAT) to improve its signal-to-noise ratio and detectability of subsurface defects. In addition, an array pickup unit of surface wave EMAT with gap configuration is proposed to receive multi-channel surface wave signals and is optimized by adjusting its coil configuration such as number, spacing and detection distance in order to obtain better SAFT imaging result. Both simulation and measured EMAT surface wave signals are used for the defect imaging and all the results verified the validity and the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract Surface defects (SDs) and subsurface defects (SSDs) are the key factors decreasing the laser damage threshold of optics. Due to the spatially stacked structure, accurately detecting and distinguishing them has become a major challenge. Herein a detection method for SDs and SSDs with multisensor image fusion is proposed. The optics is illuminated by a laser under dark field condition, and the defects are excited to generate scattering and fluorescence lights, which are received by two image sensors in a wide-field microscope. With the modified algorithms of image registration and feature-level fusion, different types of defects are identified and extracted from the scattering and fluorescence images. Experiments show that two imaging modes can be realized simultaneously by multisensor image fusion, and HF etching verifies that SDs and SSDs of polished optics can be accurately distinguished. This method provides a more targeted reference for the evaluation and control of the defects of optics, and exhibits potential in the application of material surface research.

The plasma polishing process is one of the non-conventional techniques used to remove material at the atomic level from the substrate. During the polishing of the fused silica substrate, the process parameters, namely radio-frequency (RF) power, pressure ratio (SF6/O2), and total pressure of the plasma chamber, are investigated and optimised for material removal rate (MRR) and % change in surface roughness (% ΔRa) using response surface methodology. The optimum values obtained for MRR and % ΔRa are 0.012 mm3/min and 3.59, at RF power of 60 W, pressure ratio of 3, and total pressure of 14.3 mbar. The experimental results reveal that surface roughness slightly increases from 0.344 to 0.356 μm after plasma processing at optimised process conditions. Moreover, the plasma-processed fused silica substrate is characterised using field emission scanning electron microscopy and energy dispersive X-ray spectroscopy, which depict the presence of silicon, oxygen, and fluorine on the processed substrate. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is an emerging technique for efficient fabrication of individually tailored and complex geometry parts. The fabrication process is prone to induce various defects that can have detrimental effects on the AM components. Therefore, a reliable technique that enables monitoring the integrity of AM components and in return helping to optimize the fabrication parameters in mission-critical structures is highly demanded. This work presents a fast and high-resolution damage visualization method using laser-ultrasonic (LU) imaging technique for accurately detecting and quantifying the subsurface defects in printed AM components. Specifically, a fully noncontact LU scanning system is implemented to generate and detect high signal-to-noise ratio laser ultrasonic waves using a pulsed laser and laser Doppler vibrometer, respectively. A strategy for fast defect localization using Rayleigh waves with circular scans is firstly proposed. The high-resolution 3D synthetic aperture focusing technique (SAFT) imaging with raster scans is subsequently performed focusing around the located damage areas to stereoscopically visualize and quantify the subsurface defects. The reconstructed images are further processed and improved using Gaussian filter algorithm to obtain accurate defect shapes, sizes, and positions. The feasibility of the proposed method is eventually verified on AlSi10Mg and stainless steel (316L) components containing subsurface defects with various types and dimensions. The measured sizes are well consistent with the designed values, suggesting that it is a reliable inspection method for AM parts to ensure quality control and feedback.

A new method to measure the depth of subsurface defects in additive manufacturing components is proposed based on the velocity dispersion analysis of Lamb waves by the wavelet-transform of laser ultrasound. Firstly, the mode-conversion from laser-generated surface waves to Lamb waves caused by subsurface defects at different depths is studied systematically. Secondly, an additive manufactured 316L stainless steel sample with six subsurface defects has been fabricated to validate the efficiency of the proposed method. The measured result of the defect depth is very close to the real designed value, with a fitting coefficient of 0.98. The defect depth range for high accuracy measurement is suggested to be lower than 0.8 mm, which is enough to meet the inspection of layer thickness during additive manufacturing. The result indicates that the proposed method based on laser-generated ultrasound (LGU) velocity dispersion analysis is robust and reliable for defect depth measurement and meaningful to improve the processing quality and processing efficiency of additive/subtractive hybrid manufacturing. [ABSTRACT FROM AUTHOR]

The conventional machine learning algorithm for analyzing ultrasonic signals to detect structural defects necessarily identifies and extracts either time- or frequency-domain features manually, which has problems in reliability and effectiveness. This work proposes a novel approach by combining convolutional neural networks (CNNs) and wavelet transform to analyze the laser-generated ultrasonic signals for detecting the width of subsurface defects accurately. The novelty of this work is to convert the laser ultrasonic signals into the scalograms (images) via wavelet transform, which are subsequently utilized as the image input for the pretrained CNN to extract the defect features automatically to quantify the width of defects, avoiding the necessity and inaccuracy induced by artificial feature selection. The experimentally validated numerical model that simulates the interaction of laser-generated ultrasonic waves with subsurface defects is first established, which is further utilized to generate adequate laser ultrasonic signals for training the CNN model. A total number of 3104 data are obtained from simulation and experiments, with 2480 simulated signals for training the CNN model and the remaining 620 simulated data together with 4 experimental signals for verifying the performance of the proposed algorithm. This approach achieves the prediction accuracy of 98.5% on validation set, particularly with the prediction accuracy of 100% for the four experimental data. This work proves the feasibility and reliability of the proposed method for quantifying the width of subsurface defects and can be further expanded as a universal approach to various other defects detection, such as defect locations and shapes. [ABSTRACT FROM AUTHOR]

Abstract Subsurface defects have a significant impact on the precision and performance of nano-structures. In this paper, molecular dynamics simulation of nano-indentation is performed to investigate the effect of machining-induced subsurface defects on dislocation evolution and mechanical properties of materials, in which the specimen model with subsurface defects is constructed by nano-cutting conforming to reality. The formation mechanism of subsurface defects and the interaction mechanism between machine-induced defects and dislocation evolution are discussed. The hardness and Young’s elastic modulus of single crystal copper specimens are calculated. The simulation results indicate that there exist stable defect structure residues in the subsurface of workpiece, such as atomic clusters, stacking fault tetrahedral, and stair-rod dislocations. Secondary processing of nano-indentation can restore internal defects of the workpiece, but the subsurface damage in the secondary processing area is aggravated. The nano-indentation hardness of specimens increases with the introduction of subsurface defects, which results in the formation of work-hardening effect. The existence of subsurface defects can weaken the ability of material to resist elastic deformation, in which the mutual evolution between dislocations and subsurface defects plays an important role.

A new method to measure the depth of subsurface defects in additive manufacturing components is proposed based on the velocity dispersion analysis of Lamb waves by the wavelet-transform of laser ultrasound. Firstly, the mode-conversion from laser-generated surface waves to Lamb waves caused by subsurface defects at different depths is studied systematically. Secondly, an additive manufactured 316L stainless steel sample with six subsurface defects has been fabricated to validate the efficiency of the proposed method. The measured result of the defect depth is very close to the real designed value, with a fitting coefficient of 0.98. The defect depth range for high accuracy measurement is suggested to be lower than 0.8 mm, which is enough to meet the inspection of layer thickness during additive manufacturing. The result indicates that the proposed method based on laser-generated ultrasound (LGU) velocity dispersion analysis is robust and reliable for defect depth measurement and meaningful to improve the processing quality and processing efficiency of additive/subtractive hybrid manufacturing.

A procedure was developed for detection of subsurface defects in layered composite structures by tracing dynamic changes of optical speckles on surface images directly above the defect under the impact of resonance ultrasonic excitation, as well as a procedure for determination of deformation fields near loaded holes in composites for different modes of their drilling, using a portable optico-digital correlator. The paper gives the results of investigations on determination of crushing stresses in multirow «composite-metal» bolted joints by deformation in the vicinity of an isolated hole, contact-loaded through a bolt. 13 Ref., 2 Tabl., 9 Fig. [ABSTRACT FROM AUTHOR]

• Couplingpulse eddy current sensing architecturefordeeperdefects detection. • Increase the ratio of indirect coupling energy to direct coupling energy. • Detection sensitivity of different duty cycles and peak difference are discussedforanalyzingthe defect depth. Deep defect detection for ferromagnetic materials is a challenging task for Eddy Current testing (ECT) due to the skin effect of the low penetration depth. Pulsed Eddy Current testing (PECT) is a potential and effective method for detecting deep subsurface defects as it can obtain multiple depth information owing to its wide spectrum range. A novel weak coupling sensing structure of pulsed eddy current is proposed. In particular, the structure improves the ratio of the indirect coupling energy by reducing the direct coupling energy between the excitation and the detection coils. It obviously improves the ability to detect deep subsurface defects of ferromagnetic materials. The principle of penetration and the analysis of its equivalent circuit are presented. In addition, both experiments and simulations on different defects detection have been studied. The results have confirmed that all types of defects can be detected and it has shown the relatively monotonic linear relationships and reliability. [ABSTRACT FROM AUTHOR]

The ultra-precision grinding technology based on a workpiece self-rotational principle is extensively used for silicon wafer thinning in the chip post-processing. Nevertheless, owing to the random nature of diamond grains and the unique machining manner of rotating components, accurate prediction and real-time monitoring of grinding forces remain a significant challenge in wafer self-rotational grinding (WSRG) process. This work establishes an improved theoretical model and builds a novel in-situ measurement system to analyze and detect the grinding force. Firstly, the motion trajectory and contact condition of diamond grains are explored. The grain–workpiece interactions are divided into rubbing, plowing, cutting and brittle stages. Secondly, the grinding force prediction model is proposed, which considers the combined effects of grain wear, grain randomness characteristics, brittle-to-ductile transition, elastic rebound, strain rate and grinding marks. Moreover, piezoelectric force sensors are employed to build the in-situ measurement system. The developed system is proven to simultaneously measure grinding forces online with continuous and stable output in the x , y and z directions. The impacts caused by feed rate, wheel speed and wafer speed are further revealed. Experimental results are consistent well with predicted ones, demonstrating a high prediction accuracy of the proposed grinding force model. Finally, a new index, namely the processing factor, is introduced to calculate the subsurface damage depth and residual stress for various machining parameters. This work not only enhances the understanding of predicting and measuring grinding force, but also provides a new method to evaluate the subsurface defects in brittle material grinding. [Display omitted] • Establish a prediction model of grinding force in wafer self-rotational grinding. • Consider the effect of grain wear, grain size, elastic rebound and brittle fracture. • Build an in-situ measurement system to monitor triaxial grinding force in real time. • Introduce processing factor to evaluate subsurface damage depth and residual stress. [ABSTRACT FROM AUTHOR]

In the field of advanced packaging, large-size silicon wafers are usually thinned by ultra-precision grinding technology relying on the process of workpiece self-rotation. However, the damage caused by mechanical material removal deteriorates the wafer surface flatness and die strength, while its atomic-scale formation principle has yet to be revealed. This work explores the ductile deformation and subsurface damage evolution mechanism of silicon wafer during the ultra-precision grinding process. Some grinding tests are first conducted using different diamond wheels. The surface topography and energy dispersive spectroscopy (EDS) mapping of ground wafers are measured by scanning electron microscope (SEM). The phase transition properties of the ground silicon wafer are confirmed via Raman spectrum. Next, the specimen of groove cross-section is fabricated and its morphology is examined via transmission electron microscopy (TEM) to analyze the subsurface damage characteristics. The subsurface defects at the atomic scale, involving amorphous layer, dislocations, stacking faults and lattice distortions, are observed. Due to its controllable load and similarity of the cutting motion, nanoscratch is utilized for investigating the influence of abrasive interactions on subsurface deformation during the grinding process. Thus, a series of multiple nanoscratch tests are performed in both varied and constant force modes. The material removal behaviors under different scratching conditions are illustrated. This work provides a fundamental understanding of developing a high-efficiency and low-damage thinning method for 12-inch silicon wafers. [Display omitted] • Subsurface damage evolution mechanism is revealed in wafer self-rotation grinding. • Atomic-scale defects, involving amorphous layer and stacking faults, are observed. • Nanocrystal structure and high-pressure phase transition are found in damaged zone. • Material removal behavior of multiple nanoscratch is studied for monocrystal silicon. [ABSTRACT FROM AUTHOR]

This paper develops an automatic and reliable nondestructive evaluation (NDE) technique that enables quantification of the width and depth of subsurface defects of metallic components simultaneously by using non-contact laser ultrasonic technique and identified machine learning (ML) algorithm. Twenty-two specimens with various subsurface defect dimensions are designed and fabricated for laser ultrasonic experiments, and a total of 220 labeled laser ultrasonic signals are obtained for training and verifying ML models. Twelve features, including four time-domain features (maximum, minimum, peak-to-peak, and |Neg|/Pos value of the laser generated Rayleigh ultrasonic waves) and eight wavelet energy features, are identified and extracted as sensitive feature vectors for establishing the dataset. The principal component analysis (PCA) is implemented as dimensionality reduction method of feature vectors to optimize the recognition algorithm and improve the detection accuracy. Three widely used ML models in NDE, adaptive boosting (Adaboost), extreme gradient boosting (XGBboost), and support vector machine (SVM), combined with the PCA are proposed and compared for detecting both the width and depth of subsurface defects. The PCA-XGBoost achieves the highest recognition rate of 98.48%, and is therefore identified as the most effective approach for analyzing laser-ultrasonic signals. Unlike published reports, the proposed model is trained and evaluated with experimental data covered various classification labels, which is more adaptive and reliable in practical application than the models established using simulated data or limited experimental data. In other applications, as long as sufficient laser ultrasonic data with regards to various defect properties (dimensions, orientations, locations, shapes, etc.) can be acquired, the developed approach can realize accurate detection of corresponding defects. [ABSTRACT FROM AUTHOR]

[Display omitted] • Experimental study of subsurface defect detection in concrete slabs by step heating thermography (SHT). • Analysis of absolute thermal contrast on the surface of slabs, as well as its first and second time derivatives. • Characterisation of heat transition in concrete slabs with hidden defects into three stages during heating and cooling phases. • Clear delineation of the heat transition around subsurface defect and observation of defect using infrared camera. • The understanding of heat transition around defects improves implementation of IRT for monitoring of concrete structures. Concrete is a highly consumed construction material used in the built environment and civil infrastructure. Therefore, condition monitoring using non-destructive techniques such as infrared thermography (IRT) should be optimised to extend the safe and continuous operation of existing structural asset and to reduce the requirement for new construction, which is more carbon intensive. The objective of this paper is to present an in-depth understanding of the heat flow mechanism in concrete slabs with hidden subsurface defects using IRT experiments. The experiments were performed on six concrete slabs with dimensions of 250 × 250 × 100 mm. Five of these slabs had 5 to 25 mm concrete cover over the simulated subsurface defect, and the sixth slab was plain concrete with no defect, used as control sample. Two sets of experiments using step heating thermography (SHT) technique were conducted using an IR heater as the excitation mechanism. By thermography of slabs, several sequences of IR images were recorded during both heating and cooling phases. Subsequently, the sequences of IR images were post-processed using a routine developed in MATLAB to achieve the evolution of temperature on surface of the samples. The temperature records from the tests during both heating and cooling phases were used to calculate thermal contrast, as well as first and second time derivatives of thermal contrast. By close examination of the sequences of IR images as well as sign analysis of the thermal contrast and its time derivatives, the mechanism of heat transition in defective concretes was characterised and interpreted in three stages during both heating and cooling phases. The three stages of heating phase are named as simultaneous heat-up, lateral heat flow and maximum thermal contrast. The formation of the lateral heat flow that corresponds to the transition from stage one to two causes the thermal gradient on the surface observable by the thermal camera. Likewise, three stages of cooling phase are simultaneous cool down, lateral heat diffusion from void cover to sound concrete and inverse thermal contrast. The conclusions of this study increase the basic understanding about mechanism of heat transfer in defective concrete and put forward new directions toward optimised implementation of IRT for the detection of hidden defects in concrete bridges. [ABSTRACT FROM AUTHOR]

This paper presents results from 3D photothermal reconstruction of real subsurface defects in carbon fiber-reinforced polymers using the virtual wave concept. We apply pulsed thermography during and after interrupted fatigue loading in tensile-tensile configuration for notched multidirectional laminates. In addition, we discuss the influence of the ambient conditions from in-situ photothermal testing on the virtual wave signal. The results obtained from 3D photothermal reconstruction are directly compared with those obtained from laser ultrasound testing. To summarize, we demonstrate a practicable and fast 3D photothermal evaluation of subsurface defect evolution utilizing the virtual wave concept. [ABSTRACT FROM AUTHOR]

[Display omitted] • LIPE enables low-defect machining compared to traditional laser ablation processes. • Little chemical as well as structural defects are formed due to LIP etching. • TEM characterization shows no crystal defects by the etching process. • Increase of reaction layer thickness with an increasing substrate temperature. The precise surface machining of silicon by pulsed laser processing is challenging. Laser ablation enables direct patterning but causes numerous modifications of the surface and sub-surface region. Due to the increasing demand of precisely structured silicon surfaces in various fields, such as optics and micro-electronics and micro-fluidics, new laser-based ultra-precise surface machining techniques are required. Therefore, the recently developed laser-induced plasma etching process (LIPE) was studied in relation to chemical as well as structural modifications after the etching of single-crystalline silicon. For the studies, a fs-laser (775 nm, 150 fs, 1 kHz) with a pulse energy of maximum 750 µJ was focused to a CF 4 /O 2 gas mixture at atmospheric pressure igniting a laser-induced plasma in front of the 〈1 0 0〉 Si sample. For comparison, a silicon surface was also structured by direct laser ablation. The LIP etched surface, that is characterized by SEM, TEM, XPS- and Raman-spectroscopy, shows no melting features, no structural surface or subsurface defects and almost no chemical contamination from etching besides a 2 nm thick silicon oxyfluoride on top of the atomically ordered silicon. The comparison shows clearly that the severe structural and chemical modifications at laser ablation of silicon can be avoided enabling ultraprecise surface machining. [ABSTRACT FROM AUTHOR]

In order to realize the quantitative detection of subsurface cracks, finite element method (FEM) was used to study the interaction of laser-generated Rayleigh wave and subsurface crack with different buried depths and lengths in this paper. The transmitted and reflected Rayleigh waves of subsurface cracks were analyzed in frequency domain, it is found that the center frequency of transmission wave decreases with the increase of crack length when the buried depth of crack remains unchanged, and increases with the increase of crack depth when the crack length remains unchanged. The center frequency of reflected Rayleigh wave is independent of crack length, and has a quadratic function relation with the buried depth of subsurface crack. The relationships, between the ratio of buried depth to incident Rayleigh wavelength and the ratio of reflected Rayleigh wave frequency to incident Rayleigh wave frequency, and the ratio of crack length to incident Rayleigh wavelength and the ratio of transmitted Rayleigh wave frequency to incident Rayleigh wave frequency, were obtained respectively based on the frequency analysis. The relationships were verified by the simulation signals, it is found that the maximum relative error of the buried depth of the subsurface cracks determined by the proposed relationship is 5.939%, and the maximum relative error of the length of the subsurface cracks is 12.149%. The research results show that the proposed method is promising in quantitative characterization of the buried depth and length of subsurface cracks in metallic materials. [ABSTRACT FROM AUTHOR]

Potassium dihydrogen phosphate (KDP) crystals, which are widely used in high-power laser systems, are required to be free of defects on fabricated subsurfaces. The depth of subsurface defects (SSD) of KDP crystals is significantly influenced by the parameters used in the single point diamond turning technique. In this paper, based on the deliquescent magnetorheological finishing technique, the SSD of KDP crystals is observed and the depths under various cutting parameters are detected and discussed. The results indicate that no SSD is generated under small parameters and with the increase of cutting parameters, SSD appears and the depth rises almost linearly. Although the ascending trends of SSD depths caused by cutting depth and feed rate are much alike, the two parameters make different contributions. Taking the same material removal efficiency as a criterion, a large cutting depth generates shallower SSD depth than a large feed rate. Based on the experiment results, an optimized cutting procedure is obtained to generate defect-free surfaces. [ABSTRACT FROM AUTHOR]

Surface etching of single-crystal diamond (SCD) in hydrogen plasma plays a decisive role for high-quality homoepitaxy and surface conduction of diamond. The complexity of surface/subsurface condition such as defects would give rise to the distinct morphology features and even further result in the unpredictable properties. Correspondingly, the evolutionary relationship between the morphology and electrical properties of mechanically polished SCDs which have typical cleavage damage in microns depth after hydrogen plasma treatment still needs to be further completed. In this work, the chemical vapor deposited (CVD) SCDs with uniform polishing-caused subsurface cleavage were immersed in microwave hydrogen plasma for variable duration. Time-dependent morphological evolution, multi-mode etched pits (EPs) (resulting from local defects/cracks and dislocations) and associated etching mechanism were comprehensively studied. At the early stage of plasma treatment before 60 min, owing to the obvious surface roughing generated by preferential etching (with the rate of >400 nm/h) of severe mechanically damaged regions, the hole mobility was significantly deteriorated, also accompanying with the apparent drop of sheet concentration. Concurrently, the deposited surface sp 2 amorphous carbon (a -C) nanodots on diamond surface and non- sp 3 hydrogen bonds reconstruction, which arising from defect-etching produced local abundant carbon-containing radicals or possible exfoliation generated by hydrogen penetration of massive defective regions, also would play an adverse role for the conduction associated with (100) C–H surface. However, the surface roughness was reduced from the maximum 13.2 nm–4.2 nm by prolonging etching, resulting from the smoothing effect of small-angle crystal misorientation on waved height difference related to the uniform (111) cleavage and the removal of dense irregular EPs associated with polishing-induced local defects as well as the vanishing of surface a -C nanodots. In this case, the improvement of surface electrical properties together with the negative correlation of the mobility and sheet concentration were appeared by the gradual formation of (100) diamond surface and the building of uniform (100) C–H bonds. • CVD SCDs with typical subsurface damages(defects) were treated in pure hydrogen plasma. • Morphology evolution associated with polishing defects was time-dependently presented. • Surface sp2 nanodots deposition from defect-etching and CH reconstruction would affect electrical properties. [ABSTRACT FROM AUTHOR]

Abstract: Reactions of acrolein, water, and oxygen with the vacuum-reduced surface of TiO2(110) are reported in a temperature programmed reaction study of the interaction of an aldehydic pollutant with a reducible metal oxide. A total of 25% of the acrolein that binds to the surface is converted to products. Notably, carbon–carbon coupling occurs with 86% selectivity for formation of C6 products: C6H8, identified as 1,3-cyclohexadiene, in a peak at 500K and benzene immediately thereafter at 530K. Acrolein is evolved from the surface in three peaks: a peak independent of coverage at 495K, attributed to decomposition of an intermediate that is partly converted to C6H8; a coverage-dependent peak that shifts from 370K (low coverage) to 260K (high coverage), which is attributed to adsorption at 5-fold coordinated Ti sites; and a multilayer state at 160K. Water and acrolein compete for 5-fold coordinated titanium sites when dosed sequentially. The addition of water also opens a new reaction pathway, leading to the hydrogenation of acrolein to form propanal. Water has no effect on the yield of 1,3-cyclohexadiene. Exposure of the surface to oxygen prior to acrolein dosing quenches the evolution of acrolein at 495K and concurrently eliminates the coupling. From these results, we propose that reduced subsurface defects such as titanium ion interstitials play a role in the reactions observed here. The notion that subsurface defects may contribute to the reactivity of organic molecules over reducible oxide substrates may prove to be general. [Copyright &y& Elsevier]

Abstract: In the last few years, a non-conventional ultrasonic approach has been proposed by the authors to experimentally assess the main contact parameters in wheel–rail systems. This technique, which is based on the analysis of the reflection of high frequency ultrasonic waves from the wheel–rail interface, makes it possible to obtain graphic maps from which we can observe the shape of the contact area, measure its size, and also collect qualitative information about contact pressure distribution. Furthermore, ultrasonic waves are quite well known as one of the primary non-destructive testing techniques useful in checking the presence of cracks in wheels, rails and axles. Thus, it was thought to combine these two approaches to obtain a single tool capable of supplying at the same time information about wheel–rail contact and the possible presence of subsurface cracks, which are typically located a few millimeters below the surface. This study reports the results of a series of experiments, performed on realistic wheel–rail couplings, in which the wheel was characterized by the presence of subsurface defects of known geometry. For each case tested, the whole ultrasonic waveform (which contains information about both the contact interface and a certain thickness of the wheel to investigate) was acquired and analyzed with dedicated software. The original data, expressed in terms of amplitude of reflected peaks, have been processed to obtain a number of graphic maps which, layer by layer, starting from the surface, give an idea first of all of contact conditions and, at the same time, of what kind of anomalies are present below it. These data are potentially useful in supporting numerical studies on the propagation of rolling contact fatigue (RCF) subsurface cracks by providing both actual contact pressure distribution and geometry of the cracks. [Copyright &y& Elsevier]

Abstract: In this work, focused ion beam (FIB) technique was employed to characterize subsurface defects in chromium-containing DLC (Cr-DLC) coatings. Subsurface defects as small as one micron were successfully detected in a flat Cr-DLC coated steel coupon by scanning acoustic microscopy (SAM). The nature of the subsurface defects in the coating was investigated by repeated FIB milling and a scanning electron microscopy (SEM) imaging technique where element analysis was done by Energy Dispersive X-ray spectroscopy (EDX). It has been found that defects are located mostly at the DLC steel interface. A model for the bump-like defects is proposed based on the SAM, EDX and FIB measurements. Model is that of a dilamination/void between Cr layer and steel substrate. The existence of these defects at the interface leads to the defects visible on the surface of the DLC coating; bumps or pits which are larger in diameter compared to the subsurface defects. [Copyright &y& Elsevier]

Abstract: A model for refractive index of stratified dielectric substrate was presented according to inhomogeneous coatings theories. The substrate was divided into surface layer, subsurface layer and bulk layer along the normal direction of its surface. The former two layers were equivalent to inhomogeneous coatings. Theoretical deduction was executed by employing the characteristic matrix method of optical coatings, and one mathematical calculation example was presented. The results indicate that reflectance, reflective phase shift and phase difference of polarized light deviate from ideal conditions. It shows that substrate microdefects can induce volume scattering and change propagation characteristic of light both in coatings and substrate. [Copyright &y& Elsevier]

Abstract: A model for refractive index of stratified dielectric substrate was put forward according to theories of inhomogeneous coatings. The substrate was divided into surface layer, subsurface layer and bulk layer along the normal direction of its surface. Both the surface layer (separated into N 1 sublayers of uniform thickness) and subsurface layer (separated into N 2 sublayers of uniform thickness), whose refractive indices have different statistical distributions, are equivalent to inhomogeneous coatings, respectively. And theoretical deduction was carried out by employing characteristic matrix method of optical coatings. An example of mathematical calculation for optical properties of dielectric coatings had been presented. The computing results indicate that substrate subsurface defects can bring about additional bulk scattering and change propagation characteristic in thin film and substrate. Therefore, reflectance, reflective phase shift and phase difference of an assembly of coatings and substrate deviate from ideal conditions. The model will provide some beneficial theory directions for improving optical properties of dielectric coatings via substrate surface modification. [Copyright &y& Elsevier]

• The effect of the proton irradiation dose on the properties of 4H-SiC is studied. • The directional dependence of defect-induced proton irradiation is studied. • Thermal wave scattering and IR spectroscopy are used to characterize defects. • It is shown that free carriers contribute to the in-plane thermal transport. • Scattering of thermal wave is converted into defect density depth profile. The effect of the proton irradiation dose on thermal transport anisotropy, free carrier density, and defect formation in 4H-SiC is studied by thermal wave scattering and infrared spectroscopy. Thermal waves are generated by infrared laser pulses, and the in-plane and cross-plane thermal diffusion length are measured using the deflection of a laser probe beam due to the mirage effect. The effect of proton irradiation dose on in-plane and cross-plane thermal diffusivity is measured as a function of depth. Proton irradiation is shown to cause significant damage primarily in the direction perpendicular to the sample surface. Irradiation-induced free carriers contributing to heat transport in the sample plane are revealed by Kramers-Kronig analysis of the infrared reflectivity spectra of the studied samples. Measurements of thermal diffusion lengths in the irradiated samples are converted to depth profiles of defect density. It is shown that the highly damaged zone in irradiated 4H-SiC thickens and approaches the proton reaching depth with increasing the irradiation dose. Thermal wave scattering complemented by infrared spectroscopy is proposed as an effective approach for the directional analysis of irradiation-induced changes in physical and structural properties of materials. [ABSTRACT FROM AUTHOR]

• Phase evolution of Rayleigh ultrasonic waves is reported to detect subsurface defects. • The mechanisms of phase evolution with defect width and depth are intensively studied. • The relationships between phase evolution and defect width and depth are established. • A sensitive and robust parameter is proposed to detect and quantify subsurface defect. An unreported phenomenon of phase evolution of Rayleigh ultrasonic waves with subsurface defects is observed and systematically explored for detection of subsurface defects using non-contact line laser ultrasonic technique and numerical simulation. The mechanism of phase evolution of Rayleigh wave signals is explained by the interference of the reflected and direct Rayleigh wave, explored by finite element analysis. Both experiments and simulation show distinct peak evolution of the Rayleigh wave signals with the width and depth of subsurface defect. A dimensionless parameter (|Neg|/Pos), defined by the ratio of absolute negative peak to positive peak of Rayleigh wave, is proposed to evaluate the phase evolution of Rayleigh wave with defect width and depth, which is further used to quantify the subsurface defects. The phase evolution of Rayleigh waves can act as a robust and sensitive feature to detect subsurface defects using laser-generated ultrasound, which has promising applications in life prediction and health monitoring of various engineering structures. [ABSTRACT FROM AUTHOR]

• Signal processing using SASA helps getting rid of the unwanted wave packets. • SASA leaves behind only the incident wave and its reflection from the defect. • SASA helps to bring the peak in the defect reflected wave packet in the center. • Using SASA, the reflection from the defect is identified in x, y, and z directions. The focus of this study is on locating surface and sub-surface defects that occur in rail tracks using Rayleigh waves that were emitted and sensed by a fully non-contact laser transduction system. As the quality of received signals varies with respect to the rail surface characteristics, spotting the reflection from a defect can be extremely challenging. These signals are in general contaminated with noise and have low repeatability that could hinder the proper identification of the defect signal. In view of this, an innovative signal processing technique called a self-adaptive-smart algorithm (SASA) was designed and developed. In SASA, the incident wave that is the first coming wave-packet is taken as a mother wavelet. A library of possible mother wavelets was designed based on the experimental data. As the incident wave for each sensing point varies because of the physical condition of the rail surface and the laser excitation, the algorithm automatically picks the mother wavelet from the library that generates the largest absolute cross-correlation with the incident wave. SASA is found to be able to suppress the unwanted wave packets from the overall signal leaving behind only the incident wave for a healthy specimen, and the incident wave and its reflection from the defect for a damaged specimen. The functioning of the algorithm was successfully tested by carrying out extensive experiments on a real rail track in the presence of different types of surface and sub-surface defects on its head and web. The obtained results prove the effectiveness of using SASA in localizing defects in rails with a marginal error. Notably, the proposed method has benefits such as being self-adaptive, can help suppress high levels of noise, bring the peak of defect reflected wave in the center, and distinguish between a healthy and damaged sample. [ABSTRACT FROM AUTHOR]

In this paper, the feasibility of using deep learning models (DLMs) for evaluation of bridges with overlay systems is investigated. Several laboratory-made concrete specimens with artificial subsurface defects and overlay systems (bonded and debonded) made of cement and asphalt overlay materials were tested using impact echo (IE). One-dimensional (1D) and two-dimensional (2D) convolutional neural networks (CNNs) were developed, trained, and tested on the IE data. The proposed 1D CNN was the most successful in detecting debonding and subsurface defects; it achieved an average accuracy of 0.68 on the cement overlay specimens and 0.58 for asphalt overlay specimens. Maps of the defects and debonding were generated using the DLMs and were compared to the conventional method for analyzing the IE data. The 1D CNN produced the most accurate defect maps while successfully detected sound, debonded, and defected regions, particularly on the specimens with cement overlay. • Deep learning models were designed to classify impact echo signals. • Four types of subsurface defects in bonded overlay systems were detected. • Debonding between overlay systems and bridge decks was detected. • Deep learning showed promising results compared to the peak frequency method. [ABSTRACT FROM AUTHOR]

The characteristics of laser-generated ultrasonic wave with subsurface defects are investigated by finite element method (FEM). We used this method to model a subsurface hollow with a rectangular shape, which was used to represent a subsurface defect. The diffracted bulk modes and oscillation modes result from the interaction between R-waves (Rayleigh waves) and subsurface defects are analyzed in detail. The amplitude of RE-waves (Echo waves) and O-waves (oscillating waves) results from subsurface defects change when the depth of subsurface defects increases. The sources and mechanism of E-waves and O-waves were studied. The maximum amplitude of 1–2 MHz, 2–3 MHz and 3–4 MHz changed very obvious and performed oscillation characteristics, we can select the maximum amplitude of laser-generated ultrasonic wave at different frequency can be used to detect the size and location of subsurface defect. The proposed method may provide a useful way to detect other types of subsurface structures. [ABSTRACT FROM AUTHOR]

Subsurface defects have a significant impact on the precision and performance of nano-structures. In this paper, molecular dynamics simulation of nano-indentation is performed to investigate the effect of machining-induced subsurface defects on dislocation evolution and mechanical properties of materials, in which the specimen model with subsurface defects is constructed by nano-cutting conforming to reality. The formation mechanism of subsurface defects and the interaction mechanism between machine-induced defects and dislocation evolution are discussed. The hardness and Young's elastic modulus of single crystal copper specimens are calculated. The simulation results indicate that there exist stable defect structure residues in the subsurface of workpiece, such as atomic clusters, stacking fault tetrahedral, and stair-rod dislocations. Secondary processing of nano-indentation can restore internal defects of the workpiece, but the subsurface damage in the secondary processing area is aggravated. The nano-indentation hardness of specimens increases with the introduction of subsurface defects, which results in the formation of work-hardening effect. The existence of subsurface defects can weaken the ability of material to resist elastic deformation, in which the mutual evolution between dislocations and subsurface defects plays an important role. [ABSTRACT FROM AUTHOR]