Geometrical Defects Research Articles

Effect of coaxial nozzle wear on catchment efficiency in direct energy deposition built components

Laser based Direct Energy Deposition (DED) systems using metallic powder feedstock are recognized as a promising manufacturing method for their ability to shorten production cycles and create complex part geometries. Components are built by generating a melt pool with a high-power laser beam while material is coaxially injected and left to solidify. An impediment to large scale use of DED lies in poor powder catchment efficiency, the condition in which a portion of injected powder escapes the melt pool resulting in a ratio of decreased printed material mass to mass of supplied feedstock. The wear state of a coaxial nozzle on a DED system within a hybrid manufacturing machine tool has been observed to decrease catchment efficiency over time. This study investigates this effect by adapting flow visualization techniques to an in-situ process monitoring format, the implementation of a Computational Fluid Dynamics (CFD) simulation, and deposition testing. Nozzle geometric defects due to wear are identified and categorized, and the impact of nozzle tip wear, resulting in axial tip reduction, on powder catchment efficiency is proven by multiple calculation methods. A linear correlation between catchment efficiency and powder stream diameter is identified, causing a 15–20% loss in efficiency sustained over incremental nozzle tip reduction up to − 1 mm. These results provide a foundation for further study of wear effects and Zero defect manufacturing solutions for powder fed DED systems.

Detailed characterisation and evaluation of composite stiffener R-zones based on mono-pulse ultrasonic reflection behavior

R-zones are important part of the skin-stiffener joint in composite panels. Owing to their complex geometric shapes, defect behaviour, and poor accessibility, it is difficult to perform non-destructive testing and evaluation of their defects. A new method based on reflection behaviour generated by mono-pulse ultrasonic (MU) waves in R-zones for characterising and evaluating their defects was developed. Specimens with artificial and actual delaminations were used. The MU reflection behaviours, time-domain signals, and image characteristics originating from the R-zones were studied. Optical microscopy experiments were conducted to verify the MU results. Methods for discriminating the delamination and resin layer as well as determining their accurate depth were established using the MU signals from the R-zones. The experimental results show that delamination, resin layer, and pockets in the R-zones can be characterised and evaluated in detail using the MU method. Its resolution and dead zone reached 1 prepreg ply thickness. The minimum deviation of the layup depth of the detected delamination and resin layer in the R-zone is less than one prepreg ply thickness. Delaminations of ∅ 3 mm at different depths were detected reliably.

Geometrical defect detection on additive manufacturing parts with curvature feature and machine learning

Geometrical defect detection on additive manufacturing parts with curvature feature and machine learning

Optimal thermal design of anisotropic plates with arbitrary cutouts using genetic algorithm

Anisotropic plates in different applications may have geometric defects such as openings and cracks. The presence of the opening disturbs the heat flow, which creates significant thermal stress around the opening. When the heat flux is high enough, these extreme stresses can lead to structural failure. This article aims to obtain the optimal parameters for achieving the minimum value of the normalized stress near the cutout's boundary in perforated anisotropic plates utilizing the genetic algorithm. Optimization parameters include the curvature of opening's corners, orientation angle of opening, fibers angle, heat flux angle, and opening's elongation. The plate is under heat flux, and the opening's border is thermally insulated. The stress distribution around the opening is calculated using Lekhnitskii's complex variable method and complex potential functions. The genetic algorithm is then implemented to find the optimal values for design parameters. The results show that by selecting the optimal parameters related to the anisotropic material and the opening's geometry, the stress intensity factor of the perforated anisotropic plates is remarkably reduced. Furthermore, this optimization algorithm can be extended to find the optimized parameters and achieve the optimal designs in anisotropic and isotropic perforated plates under thermal loadings.

Nanograin–glass dual-phasic, elasto-flexible, fatigue-tolerant, and heat-insulating ceramic sponges at large scales

Ceramics are considered intrinsically brittle at room temperature, which is mainly attributed to the limited availability of crystallographic slips and pre-existing geometrical flaws. Moreover, the lack of flexibility has severely hindered many high-end applications of ceramic materials. Here, we produce ceramic sponges that are simultaneously ultra-light, elasto-flexible, thermally insulating, and can fully recover from large deformation with a near-zero Poisson's ratio. These spongy materials also possess superb fatigue resistance without the accumulation of damage or structural collapse for 10,000 large-scale compressive or buckling cycles. We demonstrate the exceptional flexibility is enabled by the elastic distortion of nanograin–glassy dual phase and the fiber bulking in open-cell three-dimensional structure. Moreover, these spongy materials possess superior temperature-invariant superelasticity from deep cryogenic temperatures (−196 °C) to high temperature (1500 °C). Our study not only developed mechanically reliable lightweight ceramics for numerous extreme applications, but also provided new theoretical insights into the origin of flexibility in polycrystalline ceramics.

3D hardness profile mapping and geometrical defect prediction of laser-welded galvanized steel blanks using wobbling technology: an experimental investigation

3D hardness profile mapping and geometrical defect prediction of laser-welded galvanized steel blanks using wobbling technology: an experimental investigation

Combining wavelet analysis of track irregularities and vehicle dynamics simulations to assess derailment risks

Guaranteeing operational safety is mandatory for railways worldwide. As track deteriorates, its geometry deviates from ideal conditions (e.g. perfectly aligned and levelled), which creates undesired vibration and oscillations to the vehicle, increasing dynamic loads and derailment risk. Although current standards establish nominal limits for the track geometry, such point-wise comparisons may overlook relevant track features that can lead to unsafe conditions. The proposed investigation is a contribution to the study of the response of the rail vehicle to the track quality, aimed at identifying track irregularities that produce high track forces and unsafe vehicle responses. To fulfil this aim, Wavelet Analysis is combined with Vehicle Dynamics Simulations to evaluate how the track irregularities, filtered in various wavelength ranges and reconstructed with different wavelets and coefficients' amplitudes, impact vehicle safety in terms of the Nadal safety criterion Y/Q. The simulations demonstrate that defects occurring in some scales/frequencies are more harmful than others, and the similarity between the geometric defects and the shape of the wavelets may be used to identify track locations that require maintenance. These findings suggest that it is possible to create ‘stamps’ of track irregularities that can be used by infrastructure managers to improve their maintenance strategies.

Studying the influence of the machining process on the geometrical defects of the standardized S-shape test part

In 2020, an S-shape part was proposed as a 5-axis reception test part in the new version of ISO 10791–7:2020. This test part enables the testing of machine-tool behavior that involves a high variation in tool axis orientation. This test part is formed of an S-shape fillet which is machined in flank milling with an endmill of Ø20 mm. Any defects of the machined part are influenced by the accuracy of the CAD model, the CAM tool path computation, the measurement uncertainty of the free-formed surface by a coordinate measurement machine (CMM), and machine-tool geometric behavior. This article aims to quantify the influence of all these steps on the final defects of machined parts. The proposed conclusion to our work is based on analytical and numerical study and experimental analysis. Finally, we propose an identification process for machine-tool architecture geometrical defects pertaining to the measurement of the machined S-shape part.

Drilling of recycled carbon fibre\u2013reinforced polymer (rCFRP) composites: analysis of burrs and microstructure

Since governments encourage sustainability, industries are making great efforts to reuse or recycle carbon fibre–reinforced polymer (CFRP) composites. Despite the promising early results concerning the material properties of recycled CFRP, there is no published knowledge available about their machinability. In this study, drilling-induced micro and macro-sized geometrical defects were analysed and compared in virgin and recycled CFRP. A total of 180 drilling experiments were carried out using uncoated solid carbide cutting tools. Six different CFRP composites were tested at different feeds. The burr characteristics and microstructure were analysed by optical and scanning electron microscopy. The analysis of variance (ANOVA) results suggest that the formation of drilling-induced burrs in CFRP reinforced by recycled chopped and nonwoven mats is less pronounced than in virgin CFRP. Micro- and macro-sized geometrical defects in both recycled and virgin milled CFRP were negligible. This study found no relevant objection to using recycled CFRP from the point of view of drilling-induced burrs and microstructure damage.

Defect detection method for specular surfaces based on deflectometry and deep learning

Defect detection for specular surfaces plays a vital role in precision manufacturing. However, traditional defect detection methods are unsuitable for specular surfaces because of their specular reflection property. The defect detection on specular surfaces is usually performed by inspectors, which makes the defect detection a time-consuming and unstable task. Deflectometry has been widely used in defect detection for specular surfaces combined with machine learning. Nevertheless, conventional deflectometry methods use the local curvature deviation map based on the unwrapped phase, which can only detect geometrical defects. Moreover, hand-crafted features need to be defined for each specific task. We present a method based on deflectometry and deep learning. Deflectometry provides the input images for the network, and the deep learning network completes the identification and location of defects. In deflectometry, the proposed method uses the light intensity contrast map to replace the local curvature map, which can detect both geometrical and textural defects. Based on conventional networks, depthwise separable convolution kernel is applied to reduce parameters, and residual convolution block is utilized to alleviate vanishing or exploding gradients. A subnet for feature aggregation is used to obtain multiscale information of defect features. Performance evaluation based on experiment results proved the effectiveness of the proposed method.

Study on the buckling behavior of steel plate composite walls with diamond arranged studs under axial compression

In this paper, ten steel plate composite wall specimens with studs in a diamond arrangement and a rectangular arrangement, and one parallel specimen with steel-bars trusses were tested under axial compression. The buckling stress of steel plate and the load-displacement curves for experimental parameters, including solder joint spacing-to-thickness ratio, spacing ratio, stud arrangement shape and connector type, are analyzed. With the solder joint spacing-to-thickness ratio decreasing, both the axial compressive strength of specimens and buckling stress of steel plates increase. Based on the test, the finite element models with the studs simulated as the solid element to consider the contact between concrete and studs are established and validated against the test results. The parametric analysis about the buckling stress of steel plate and stress state of studs are both conducted involving the solder joint spacing-to-thickness ratio and initial geometrical defect. When the increase in the initial geometrical defect coefficient, the outward and average buckling stresses of steel plate decrease, but the maximum tensile stress of the studs first increases then decreases and does not exceed the yield stress of studs. The equations for predicting the buckling stress of steel plates are proposed, providing the reference for the design of steel plate composite walls.

Automated classification of thermal defects in the building envelope using thermal and visible images

ABSTRACT The first step in establishing a retrofit strategy for an existing building is to identify the type of thermal defects in the building envelope. Infrared thermography is mainly used to detect thermal defects. However, the diagnosis results are subjectively influenced by the auditor’s experience. This study proposes a method for classifying thermal defects into material-related thermal bridges, geometrical thermal bridges, air leakages, and other thermal defects via thermal and visible images. To verify the performance of the proposed method, a field experiment was performed on a building in which thermal defects occurred. The results of the field experiment showed that the F-scores of the proposed method were 0.9707 for air leakage, 0.9000 for a material-related thermal bridge, 0.9775 for a geometrical thermal bridge, and 0.9228 for other defects. The results of this study show the potential for automatically classifying various types of defects that occur in building envelopes.

Ab-Initio Investigation of Finite Size Effects in Rutile Titania Nanoparticles with Semilocal and Nonlocal Density Functionals

In this work, we employ hybrid and generalized gradient approximation (GGA) level density functional theory (DFT) calculations to investigate the convergence of surface properties and the electronic gap of rutile titania (TiO2) nanoparticles with particle size. The surface energies and electronic gaps are calculated for cuboidal particles with minimum dimension ranging from 3.7 Å (24 atoms) to 10.3 Å (384 atoms) using a highly parallel real-space DFT code to enable hybrid-level DFT calculations of larger nanoparticles than are typically practical. We deconvolute the geometric and electronic finite size effects in surface energy and evaluate the influence of defects on the electronic gap and density of states (DOS). The electronic finite size effects in surface energy vanish when the minimum length scale of the nanoparticles becomes greater than 10 Å. We show that this length scale is consistent with a computationally efficient numerical analysis of the characteristic length scale of electronic interactions. The surface energy of nanoparticles having a minimum dimension beyond this characteristic length can be approximated using slab calculations that account for the geometric defects. In contrast, the finite size effects on the electronic gap and DOS are highly dependent on the shape and size of these particles. The DOS for cuboidal particles and more realistic particles constructed using the Wulff algorithm reveal that defect states within the electronic gap play a key role in determining the eigenvalue distribution of nanoparticles and the electronic gap does not converge to the bulk limit for the particle sizes investigated.

A generalized Bayesian regularization network approach on characterization of geometric defects in lattice structures for topology optimization in preliminary design of 3D printing

A generalized Bayesian regularization network approach on characterization of geometric defects in lattice structures for topology optimization in preliminary design of 3D printing

Investigating Surface Fractures and Materials Behavior of Cultural Heritage Buildings Based on the Attribute Information of Point Clouds Stored in the TLS Dataset

To date, the potential development of 3D laser scanning has enabled the capture of high-quality and high-precision reality-based datasets for both research and industry. In particular, Terrestrial Laser Scanning (TLS) technology has played a key role in the documentation of cultural heritage. In the existing literature, the geometric properties of point clouds are still the main focus for 3D reconstruction, while the surface performance of the dataset is of less interest due to the partial and limited analysis performed by certain disciplines. As a consequence, geometric defects on surface datasets are often identified when visible through physical inspection. In response to that, this study presents an integrated approach for investigating the materials behavior of heritage building surfaces by making use of attribute point cloud information (i.e., XYZ, RGB, reflection intensity). To do so, fracture surface analysis and material properties are computed to identify vulnerable structures on the existing dataset. This is essential for architects or conservators so that they can assess and prepare preventive measures to minimize microclimatic impacts on the buildings.

The failure behavior of buckling pin valve: Theoretical, numerical, and experimental study

At present, buckling pin in the bypass of piping as pressure relief valve has been gradually utilized in the low-concentration coal-bed methane (CBM), which bends to release pressure when the main valve fails leading to pipeline blockage. However, current researches mainly focused on the buckling behavior of hydraulic cylinder rod or rod string, and less consideration was given to the operational reliability of buckling pin valves. This paper deduced the calculation formula of the critical failure load based on Euler formula in the buckling pin under buckling load. Besides, three finite element models (FEM) based on Johnson−Cook constitutive model were compared to predict failure strength of buckling pin which were verified by experiment. In addition, the defect sensitivity analysis of the buckling pin under different initial geometric defects rate was carried out. The results showed that a) the experimental value of the critical failure load in the buckling pin was 206.04 N and the bending position was in the middle of the buckling pin; b) the analysis result adopting explicit dynamic method was in best agreement with the experimental results within deviation of 0.24%; and c) the initial geometric defect of buckling pin should be controlled within 1%. This study provides an important reference to predict the critical failure load of the buckling pin valve and achieve safe transportation of low-concentration CBM.

Real-time multiscale prediction of structural performance in material extrusion additive manufacturing

The material extrusion additive manufacturing (AM) has been extensively used in fabricating structures with complex geometries. However, geometric defects often exist in an AM structure, which could compromise its final performance. In this paper, a real-time multiscale performance evaluation method is developed for material extrusion-based honeycomb structures. The representative cell boundary is extracted from three-dimensional (3D) point clouds obtained via an in-situ monitoring approach. The cell boundary is then used to generate the digital twin of the unit cell of the printed layer based on the finite element (FE) method. A physics-based multiscale modeling approach called mechanics of structure genome (MSG) is then employed to predict the effective material properties of the printed layer and plate stiffness matrix of the final structure. The proposed approach provides a highly efficient way to predict the real-time performance of the as-manufactured products. Moreover, the numerical example shows that the geometric defects could result in complex mechanical behaviors in the defected parts, which cannot be captured by the conventional approaches based on the shape deviations. The numerical results are validated by the three-point bending tests. The proposed method can be used in the closed-loop control of material extrusion-based manufacturing systems.

Feasibility Study of Drone-Based 3-D Measurement of Defects in Concrete Structures

Recognition of defects in concrete structures, identification of cracks, concrete spalling, or other geometrical defects are important tools for structural damage detection. Defects in structures can include cracks, but also missing parts, due to the wear caused by weather or aging phenomena. These last types of defects in structures can be identified using red-green-blue (RGB) cameras, but the level of damage could be difficult to evaluate with 2-D images. In this sense, the application of 3-D reconstruction techniques can be helpful to determine the 3-D dimensions of spalling, swelling of concrete or the presence of visible steel parts of reinforced concrete. The use of drones for this type of measurement is very attractive for reducing the costs and time of measurement campaigns. However, the lack of accurate trajectory information and the vibrations affect the accuracy of 3-D measurements. In this article, a metrological characterization of measurement systems for the evaluation and recognition of defects in concrete structures is presented, starting from the acquisition of 3-D point clouds by low-cost time-of-flight (ToF) sensors, placed on drones. To evaluate the uncertainty of these systems, a mock-up with realistic defects was developed and characterized using a reference 3-D scanner. The 3-D reconstructions obtained via the selected sensors were used to evaluate the discrepancies of the 3-D shape compared to a ground truth model and the uncertainty of the selected scanners. The results show that, for all the defects tested, the standard deviation of the discrepancies between the defect reconstructed using the drone and the ground truth is below 2.5 mm.

Optical Modeling and Characterization of Demyelinated Nerve Using Graphene-Based Photonic Structure

The myelin sheath, as an insulation layer in nerve cells, plays an essential role in neural communication and signal conduction. Loss of myelin, referred to as demyelination, is associated with many mental disorders. Detecting the tiny demyelinated parts in nerve fibers can provide early diagnosis of some mental disorders and create effective treatment plans. This paper establishes a new engineering approach for differentiation between demyelinated and myelinated axons by analyzing spectral responses resulting from the optical simulation framework. We propose computational modeling on the photonic communication of nerve fibers and develop a graphene-based neurophotonic device that can be used to detect the regions demyelinated on the nerve fiber. We first model a nanoscale thin-film configuration of the multilayered myelinated axon to evaluate photons transmission in the nerve fibers under geometric defects as demyelination. Then, the nerve’s optical characteristics are achieved by focusing on the reflectance of light incidence on the nerve model with the change of the demyelination size to distinguish demyelinated—from myelinated nerves by the spectral contrast. Undertaking the different levels of demyelination progression, we theoretically explore the variations of effective refractive index using an analytical solution technique. Ultimately, we design a nanostructure configured with silicon dioxide, graphene, and gold nanoparticles to function as a biochip recognizing myelinated axon damage under the surface plasmon effect. This device can promote a practical procedure to distinguish nanoscale demyelinated and myelinated axons, which can be utilized for neural sensing of tiny brain tissues as a neurophotonic needle.

Geometrical Configuration Modulation Via Ion Doping and Defect Engineering in Spinel Oxides for Enhanced Oxygen Revolution Activity

Geometrical Configuration Modulation Via Ion Doping and Defect Engineering in Spinel Oxides for Enhanced Oxygen Revolution Activity