Geometrical Defects Research Articles

Framework of Scan to Building Information Modeling for Geometric Defect Localization in Railway Track Maintenance

Railway transportation plays a vital role in modern society, enabling the safe and efficient movement of people and goods over long distances. To ensure the longevity and safety of a railway infrastructure, the regular maintenance of tracks is crucial. Traditional track inspections, conducted manually to monitor geometric parameters and to identify defects, are time-consuming, labor-intensive, and prone to human error. Current Scan-to-BIM frameworks for railway maintenance also lack standardized methods for extracting geometric parameters that can be easily integrated into Building Information Modeling (BIM). Additionally, the Industry Foundation Classes (IFC) standard, used for BIM data exchange, does not support storing parameter values at specific chainage points along the track, limiting defect localization. A framework is proposed to address these challenges by standardizing the extraction of geometric parameters from point cloud data and ensuring seamless integration with BIM. The framework calculates parameters at station chainage points and generates additional chainage points along the track, associating the data with the corresponding chainage. A case study demonstrates the framework’s ability to enhance defect localization, using the EN 13848-5 European Standard to identify defects at specific chainages. Ultimately, this approach contributes to the more effective lifecycle management of railway tracks.

Electronic States and transmission in GaAs/GaAlAs multi-quantum wells with geometrical defects

In this paper, we use the Green’s function approach to conduct the theoretical study of the propagation of electron waves in a multi-quantum wells (MQWs) made up of GaAs and GaAlAs layers with a periodic structure. Localized electronic states are produced inside the band gaps due to the presence of defects of various types inside the MQWs. These states are extremely sensitive to the thicknesses and the position of the different inserted defect layers. The transmission rate of these states is always at its highest as the number of defects rises. Similar to this, we found that the transmission rates of these defect layers decrease the further apart they are. Thus, when the defect position is separated by more than two cells, this kind of interaction is stronger; however, when they are brought closer, it is weaker. Due to the energy transfer between the various electronic states created inside the band gaps, the origin of the states induced by the wells defect becomes a state induced by the barrier defect, and vice versa. This causes a change on the behavior of the induced electronic localized states.

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.

Influence of geometrical defects on aerodynamic drag of non-watertight Ahmed body

Influence of geometrical defects on aerodynamic drag of non-watertight Ahmed body

ZnIn2S4 nanosheets with geometric defects for enhanced solar-driven hydrogen evolution and wastewater treatment

The key to development of high-performance and low cost photocatalysts hydrogen evolution and waste degradation is to realize efficient photo-generated charge carrier separation and surface catalytic reaction simultaneously without noble metal co-catalyst. To achieve this goal, a noble metal free ZnIn2S4 photocatalyst with geometric defects has been constructed, in which [In-S]4 tetrahedral vacancies (named as (InS)v) on the surface are created by removing the In and S atoms. Further mechanistic investigation has revealed that the (InS)v not only works as the active sites for reductive reaction, but also improves photo-generated electron-hole separation. As a result, the solar-driven hydrogen evolution rate reaches 10.70 mmol/gh without co-catalyst after optimizing the densities of the (InS)v. The apparent quantum efficiency at 380 nm, 395 nm, 420 nm, 450 nm and 520 nm is as high as 14.0 %, 17.1 %, 12.1 %, 8.2 % and 2.4 %, respectively. In addition, these photocatalysts also achieve a rate of 97.78 % and 91.27 % of RhB and MO within 3 and 60 min, respectively. This work show-case a novel approach to develop dual functional and noble metal free photocatalysts for efficiently utilizing solar energy for hydrogen generation and wastewater treatment.

Seamless and Aligned Texture Optimization for 3D Reconstruction

AbstractRestoring the appearance of the model is a crucial step for achieving realistic 3D reconstruction. High‐fidelity textures can also conceal some geometric defects. Since the estimated camera parameters and reconstructed geometry usually contain errors, subsequent texture mapping often suffers from undesirable visual artifacts such as blurring, ghosting, and visual seams. In particular, significant misalignment between the reconstructed model and the registered images will lead to texturing the mesh with inconsistent image regions. However, eliminating various artifacts to generate high‐quality textures remains a challenge. In this paper, we address this issue by designing a texture optimization method to generate seamless and aligned textures for 3D reconstruction. The main idea is to detect misalignment regions between images and geometry and exclude them from texture mapping. To handle the texture holes caused by these excluded regions, a cross‐patch texture hole‐filling method is proposed, which can also synthesize plausible textures for invisible faces. Moreover, for better stitching of the textures from different views, an improved camera pose optimization is present by introducing color adjustment and boundary point sampling. Experimental results show that the proposed method can eliminate the artifacts caused by inaccurate input data robustly and produce high‐quality texture results compared with state‐of‐the‐art methods.

Fatigue Scattering Analytics and Prediction of SS 316L Fabricated by Laser Powder Bed Fusion

Abstract Laser powder bed fusion (LPBF) is an enabling process manufacture of complex metal components. However, LPBF is prone to generate geometrical defects (e.g., porosity, lack of fusion), which causes a significant fatigue scattering. However, LPBF fatigue scattering data and analysis in the literature are not only sparse and limited to tension-compression mode but also inconsistent. This article presents a robust high-frequency fatigue testing method to construct stress-cycle curves of SS 316L to understand the scattering nature and predict the scattering pattern. A series of bending fatigue tests are performed at different stress amplitudes. Two different runout criteria are used to investigate fatigue life, fatigue limits, and scattering. The endurance limit reaches around 300 MPa for the defect size distribution at the selected process space. The defect size-based fatigue limit model is found to underestimate the endurance limit by about 30 MPa when comparing with the experimental data. Fatigue scattering is further calculated by using 95% prediction intervals, showing that low fatigue scattering is present at high stresses while a large variation of fatigue life occurs at stresses near the knee point.

Unsupervised domain adaptation for drive-by condition monitoring of multiple railway tracks

Monitoring railway tracks through drive-by vibration data collected by in-service trains offers a cost-effective and adaptable solution for inspecting multiple railway lines. However, numerous existing drive-by monitoring methods rely on supervised learning models, necessitating extensive labelled data for each line. In this paper, a novel framework is proposed based on Unsupervised Domain Adaptation (UDA) concept which facilitates the transfer of a geometric defects diagnosis model learned from one line to a new line without the need for any labelled data from the new line. The proposed framework learns the dynamic-based features that are sensitive to damage and also invariant to different railway tracks. It comprises three components: data pre-processing, UDA implementation, and damage diagnosis. The framework uses the data from the source domain, including corresponding labels, as well as the unlabelled data from the target domain as input. The outputs of the framework consist of the predicted labels for the target domain. The performance of the proposed framework is evaluated using a comprehensive dataset of field measurements of a high-speed train passing 4 different lines within the French high-speed rail network. The proposed UDA framework is implemented using four common UDA algorithms including Information-Theoretical Learning (ITL), Geodesic Flow Kernel (GFK), Transfer Component Analysis (TCA), and Subspace Alignment (SA). The results show that the proposed framework has a 14% increase in the anomaly detection accuracy compared to traditional unsupervised learning methods in which UDA is not used. Furthermore, this study investigates the impact of incorporating a percentage of target data labels during training (semi-supervised domain adaptation), along with various sensor layouts and different tuning parameters, on the accuracy of the proposed approach. The results show that the proposed framework can significantly facilitate the monitoring of railway track conditions using the data collected by in-service trains which could be great interest of railway owners.

A plastic correction algorithm for full-field elasto-plastic finite element simulations: critical assessment of predictive capabilities and improvement by machine learning

AbstractThis paper introduces a new local plastic correction algorithm that is aimed at accelerating elasto-plastic finite element (FE) simulations for structural problems exhibiting localised plasticity (around e.g. notches, geometrical defects). The proposed method belongs to the category of generalised multi-axial Neuber-type methods, which process the results of an elastic prediction point-wise in order to calculate an approximation of the full elasto-plastic solution. The proposed algorithm relies on a rule of local proportionality, which, in the context of J2 plasticity, allows us to express the plastic correction problem in terms of the amplitude of the full mechanical tensors only. This lightweight correction problem can be solved for numerically using a fully implicit time integrator that shares similarities with the radial return algorithm. The numerical capabilities of the proposed algorithm are demonstrated for a notched structure and a specimen containing a distribution of spherical pores, subjected to monotonic and cyclic loading. As a second point of innovation, we show that the proposed local plastic correction algorithm can be further accelerated by employing a simple meta-modelling strategy, with virtually no added errors. At last, we develop and investigate the merits of a deep-learning-based corrective layer designed to reduce the approximation error of the plastic corrector. A convolutional architecture is used to analyse the neighbourhoods of material points and outputs a scalar correction to the point-wise Neuber-type predictions. This optional brick of the proposed plastic correction methodology relies on the availability of a set of full elasto-plastic finite element solutions to be used as a training data-set.

Effect of geometric defects on the mechanical properties of additive manufactured Ti6Al4V lattice structures

Lattice structures realized by additive manufacturing (AM) have the great potential for a broad range of engineering applications. However, the lattice structures are involved in geometric defects. This paper focuses the effect of geometric defects on the mechanical properties of Ti6Al4V lattice structures manufactured by laser powder bed fusion (L-PBF), three cell topologies, i.e., body centered cubic with vertical struts (BCCZ), face centered cubic with vertical struts (FCCZ), and face and body centered cubic with vertical struts (FBCCZ) were studied. X-ray computed tomography was used to extract the shape and the distribution of process-induced geometric defects of these three kinds of samples. Probability density distributions of geometric defects in each layer were also established to analyze the effect of the printing sequence on geometric defects. Then these distributions of geometric defects were inputted into Abaqus to build the modified statistical models to study the effect of geometric defects on mechanical properties. It is shown that the deviation of the cross-section radius exhibits normal distribution and the deviation of the center axis offset exhibits logarithmic distribution. And the middle layer of the sample has a better manufacturing precision. The modified statistical model can predict the mechanical properties within an error of 5 %. The strut thickness deviation had a more significant effect on the mechanical properties than the strut waviness.

Imaging and reconstruction of asymmetric wrinkles in carbon fibre composites using high-frequency eddy current and full matrix capture-based ultrasound

Ply wrinkles are a common defect found in Carbon Fiber Reinforced Polymer (CFRP) laminates that can lead to failure in composite structures if not correctly evaluated during manufacture stages. The mechanical performance of a structure depends on multiple parameters of a wrinkle, such as amplitude, wavelength, and asymmetry, which ideally need to all be measured to characterise the wrinkle properly. To address this issue, the authors designed and manufactured a set of complex wrinkles with various asymmetry offsets and amplitude and obtained raw data by testing them with a high-frequency eddy-current system. A unique trajectory in the complex Nyquist plot was found on asymmetric wrinkles, demonstrating the dominant influence of wrinkle asymmetry on electrical resistance. The authors also used full matrix capture ultrasound to characterise the wrinkles and measured the profiles by extracting texture phase randomness. The study revealed the superior capability of FMC UT for characterising and retrieving the profile in wrinkle coupons, while HF ECT was more adaptable for identifying the region of interest effectively. This work also implements a state-of-the-art deep fusion strategy to demonstrate a lightweight architecture can be easily used to combine multiple sources of wrinkle profiles and generate a more accurate prediction than traditional methods. Overall, the demonstrated work can be further applied to CFRPs with increasing geometric complexity and defects to facilitate the design efficiency and testing of CFRP components.

Band structures based phononic crystals in quasi-Sierpinski fractals with defects

This study presents a detailed analysis of the effects of geometric defects in phononic crystals, focusing on the use of a distribution of Sierpinski fractals for the strategic placement of inclusions. Using theoretical and computational approaches, we investigate the influence of these defects on the material's band structure. Phononic crystals, designed based on fractal geometry inspired by the Sierpinski pattern, are particularly sensitive to the introduction of inclusions, resulting in disorder in the periodic structure. Our study examines in detail how different defect configurations affect the formation of localized modes and the energy distribution in the material's band structure. The results obtained provide valuable insights into how the presence of geometric defects can be exploited or minimized in the design of phononic crystals for practical applications, including acoustic isolation, vibration control, and the development of advanced acoustic devices. These results contribute to the understanding and advancement of fractal phononic crystals, paving the way for future research and promising technological applications, both in terms of frequency and attenuation, depending on the complexity and hierarchy of the fractal structures used.

Simulation Research on the Control Method of Bow-Collapse in Gear Cold Roll-Beating

Bow-collapse is a type of geometric defect in the gear cold roll-beating process. In order to effectively control bow-collapse and improve material utilization, this paper calculates the cross-section radius of the blank according to the volume invariance principle of metal plastic deformation, and then performs FE simulation of the cold roll-beating process by using the cyclic beating of adjacent tooth spaces and intermittent blank feeding method. The flow state of metal particles is investigated, revealing that the movement of metal particles along the axial direction of the blank is the main reason for the formation of bow-collapse. This paper proposes a method to correct the cross-section radius of the blank and thus control for bow-collapse, where the loss coefficient K characterizes the state of metal particle loss in an infinitesimal thickness region on the cross-section. The analytical equation for calculating the corrected value of the cross-section radius is derived, and the corrected value is calculated. Conducted on the modified blank, cold roll-beating FE simulation results show that bow-collapse is effectively controlled, and the loss coefficient K correctly reflects the loss state of metal particles on the cross-section. The simulation results also show that after cold roll-beating, the gear teeth with standard face width and tooth depth can be obtained after two turning end faces and turning tip circle processes. The bow-collapse control method proposed in this paper effectively improves material utilization.

Deep learning of 3D point clouds for detecting geometric defects in gears

Geometric integrity directly impacts the functionality, reliability, and safety of final manufactured products, making the qualification of parts based on measurements of their geometry a fundamental quality control activity in modern manufacturing. Recent advancements in three-dimensional (3D) metrology technologies have enabled fine-scale inspection of geometric integrity characterized by dimensional accuracy, surface quality, and shape conformity. However, the widespread adoption of high-resolution 3D metrology in manufacturing faces some significant challenges posed by the inherent data structures of 3D point clouds such as high dimensionality, unstructured nature, and sparsity in defective regions. To address these challenges, this paper first creates a “MFGNet-gear” dataset, which is a scalable and comprehensive benchmark dataset comprising 12 part designs with four quality classes for each design. Subsequently, we develop a deep learning model adapted from the PointNet++ architecture to enable automated, end-to-end analysis of 3D point clouds. The model can be configured for different decision-making tasks including part design classification and multi-class geometric defect detection. Implementations of the proposed model on the “MFGNet-gear” dataset achieve accuracies up to 100% in classifying gear designs and up to 85% in four-class quality inspection. Additionally, we systematically investigate the impacts of measurement resolution and precision on the classification performance through a series of case studies. The obtained results highlight the potential of using deep learning methods for automated analysis of 3D point clouds for a variety of quality control tasks beyond gear manufacturing. This study also proposes future research directions, including the development of new deep learning architectures specifically designed for manufacturing 3D point clouds and strategies for adaptive measurement planning.

Low Ru doping induced interface and defects engineering in 2D square micro-mesoporous CoNiRuOx nanosieves for advanced oxygen evolution electrocatalysis

Efficient oxygen evolution reaction (OER) catalysts require the reasonable integration of geometric architecture, defects construction and interfacial electronic structure, which is difficult to combine multiple advantages into one low-cost catalysts. Herein, we designed a novel low Ru doping 2D square CoNiRuOx nanosieves (NSs) with abundant surface micropore and mesopore structure, rich oxygen defects and heterophase interfaces. Owing to the Ru incorporation, the electrons in Ni2+ could partially spontaneously transfer to the Ru4+ species by the bridge O2− with π donation effect according to the proposed “Ni-O-Co-O-Ru-O-Ni” electron interaction model. Benefitting from the porous surface with rich mass transfer channel, increased oxygen defects concentration, well-optimized electron redistribution, the CoNiRuOx nanosieves possessed a low overpotential of 261 mV to reach the current density of 10 mA cm−2, which is better than that of counterpart CoNiOx NSs and commercial RuO2 catalysts. The CoNiRuOx NSs also possessed the favorable durability with 50 h. Moreover, the CoNiRuOx//Pt/C electrode couple exhibited enhanced overall water splitting performance. This work provides offers insightful significance to design 2D micro-mesoporous materials for the robust electrocatalysis processes related to energy conversion technologies.

Uncertainty quantification of acoustic metamaterial bandgaps with stochastic material properties and geometric defects

Acoustic metamaterials are a subject of increasing study and utility. Through designed combinations of geometries with material properties, acoustic metamaterials can be built to arbitrarily manipulate acoustic waves for various applications. Despite the theoretical advances in this field, however, acoustic metamaterials have seen limited penetration into industry and commercial use. This is largely due to the difficulty of manufacturing the intricate geometries that are integral to their function and the sensitivity of metamaterial designs to material batch variability and manufacturing defects. Capturing the effects of stochastic material properties and geometric defects requires empirical testing of manufactured samples, but this can quickly become prohibitively expensive with higher precision requirements or with an increasing number of input variables. This paper demonstrates how uncertainty quantification techniques, and more specifically the use of polynomial chaos expansions and spectral projections, can be used to greatly reduce sampling needs for characterizing acoustic metamaterial dispersion curves. With a novel method of encoding geometric defects in a 1D, interpretable, resolution-independent way, our uncertainty quantification approach allows for both stochastic material properties and geometric defects to be considered simultaneously. Two to three orders of magnitude sampling reductions down to ∼100 and ∼101 were achieved in 1D and 7D input space scenarios, respectively. Remarkably, this reduction in sampling was possible while preserving accurate output probability distributions of the metamaterial performance characteristics (bandgap size and location).

Exponentially localized interface eigenmodes in finite chains of resonators

AbstractThis paper studies wave localization in chains of finitely many resonators. There is an extensive theory predicting the existence of localized modes induced by defects in infinitely periodic systems. This work extends these principles to finite‐sized systems. We consider one‐dimensional, finite systems of subwavelength resonators arranged in dimers that have a geometric defect in the structure. This is a classical wave analog of the Su–Schrieffer–Heeger model. We prove the existence of a spectral gap for defectless finite dimer structures and find a direct relationship between eigenvalues being within the spectral gap and the localization of their associated eigenmode. Then, for sufficiently large‐size systems, we show the existence and uniqueness of an eigenvalue in the gap in the defect structure, proving the existence of a unique localized interface mode. To the best of our knowledge, our method, based on Chebyshev polynomials, is the first to characterize quantitatively the localized interface modes in systems of finitely many resonators.

Effect of ply misalignment on the notched strength of composite laminates

Predicting the notched strength of carbon fiber-reinforced polymers is a crucial aspect of composite structure design, particularly when considering the uncertainties stemming from geometric features, material variability and defects. This study focuses on the influence of ply misalignment at the meso-scale level. The research employs a comprehensive methodology to establish notched strength allowables, integrating analytical (low fidelity), which have limitations in the representation of stacking sequence effects and in the generation of ply misalignments, and computational tools (high fidelity), employing a finite element model (FEM). The investigation emphasizes the need for a holistic understanding of these factors to enhance the accuracy of predictions. The results predicted by the analytical and, specially, the numerical models are in good agreement with experimental results. Both models underscore the decrease of the notched strength due to ply misalignment. Finally, a hybrid approach is proposed given that FEM predictions offer more accuracy and a detailed comprehension of the failure mechanisms, while fast analytical models can be used to propagate the uncertainties and to determine the allowables.

Effect of post weld heat treatment on the springback of dissimilar aluminium and steel tailor welded blanks fabricated using friction stir welding

ABSTRACT Springback is a common geometrical defect that occurs in the sheet metal forming process and depends on material properties. Different methods have been used to compensate or minimise the springback effect to date. One of the methods is post-weld heat treatment. However, not all materials are heat treatable. In this study, post-weld heat treatment effect on the springback of dissimilar materials AA6061 and SAE1020 fabricated by friction stir welding was investigated using free bending. Different artificial ageing times during the heat treatment process were explored to identify the optimum duration to minimise springback via experiment and finite element analysis. Punch stroke and punch offset tests were conducted to evaluate the effect of multiple parameter values on springback. It was found that an artificial ageing time of 6 hours results in the lowest springback. Springback is inversely proportional to punch stroke as higher punch strokes produce lower springback. For punch offset, the springback increases as the punch is offset towards AA6061 and decreases when offset towards SAE 1020. Interestingly, samples that went through post-weld heat treatment recorded lower springback on both sides even for non-treatable material. The experimental results show a similar pattern and acceptable tolerance compared to the simulation results.

Accurate modeling and safety evaluation of dented pipeline with internal pressure based on reverse modeling technique

ABSTRACT As a common form for pipeline geometric defects, dents can induce the local stress concentration of the pipeline, which would result in the debilitation of the bearing capacity and bring huge safety hazards. In this paper, the accurate surface profiles of dented pipeline are acquired based on the 3D scanning technology and deformation detection technology, respectively. Then, the dented pipeline models are constructed by the reverse modeling technique, and the stress distribution characteristics of the dented pipeline in the operation condition are simulated using ABAQUS. The maximum von Mises stresses of a dented pipeline obtained from two proposed modeling techniques are basically consistent. However, the deformation detection technology can effectively avoid the excavation work for buried pipelines. Therefore, the reverse modeling technique based on the deformation detection technology has strong feasibility in practical projects and significant application value in the safety assessment of submarine pipelines, where excavation work is impractical.