Accurate Geometry Research Articles

Optical tomography by laser line scanning and digital twinning for in-process inspection of lattice structures in material extrusion

One of the challenges of manufacturing hollow parts featuring internal lattice structures by using material extrusion is to achieve geometric accuracy of the internal geometries. In this work a solution for in-process geometric inspection and monitoring is presented, based on combining on-machine part measurement by laser line scanning and digital twinning. In the solution, a laser line scanner is used to acquire a two-dimensional map of material and void distribution within the deposited layer. Layer inspection is carried out by comparing the 2D map with a reference one obtained by simulating the deposition process (digital twin of the layer); discrepancies are automatically identified and quantified. The evolution of anomalies across layers can be tracked by vertically stacking both layer measurements and 2D digital twins and by investigating the resulting 3D voxel models. The models are updated after the fabrication of each new layer, to allow geometric monitoring over time. The proposed inspection and monitoring solution is particularly suitable for hollow parts and/or lattice or otherwise reticular internal structures, which would otherwise be inaccessible when using conventional measurement methods on the final part.

Ontology for BIM-Based Robotic Navigation and Inspection Tasks

The availability of inspection robots in the construction and operation phases of buildings has led to expanding the scope of applications and increasing technological challenges. Furthermore, the building information modeling (BIM)-based approach for robotic inspection is expected to improve the inspection process as the BIM models contain accurate geometry and relevant information at different phases of the lifecycle of a building. Several studies have used BIM for navigation purposes. Also, some studies focused on developing a knowledge-based ontology to perform activities in a robotic environment (e.g., CRAM). However, the research in this area is still limited and fragmented, and there is a need to develop an integrated ontology to be used as a first step towards logic-based inspection. This paper aims to develop an ontology for BIM-based robotic navigation and inspection tasks (OBRNIT). This ontology can help system engineers involved in developing robotic inspection systems by identifying the different concepts and relationships between robotic inspection and navigation tasks based on BIM information. The developed ontology covers four main types of concepts: (1) robot concepts, (2) building concepts, (3) navigation task concepts, and (4) inspection task concepts. The ontology is developed using Protégé. The following steps are taken to reach the objectives: (1) the available literature is reviewed to identify the concepts, (2) the steps for developing OBRNIT are identified, (3) the basic components of the ontology are developed, and (4) the evaluation process is performed for the developed ontology. The semantic representation of OBRNIT was evaluated through a case study and a survey. The evaluation confirms that OBRNIT covers the domain’s concepts and relationships, and can be applied to develop robotic inspection systems. In a case study conducted in a building at Concordia University, OBRNIT was used to support an inspection robot in navigating to identify a ceiling leakage. Survey results from 33 experts indicate that 28.13% strongly agreed and 65.63% agreed on the usage of OBRNIT for the development of robotic navigation and inspection systems. This highlights its potential in enhancing inspection reliability and repeatability, addressing the complexity of interactions within the inspection environment, and supporting the development of more autonomous and efficient robotic inspection systems.

NeuralTO: Neural Reconstruction and View Synthesis of Translucent Objects

Learning from multi-view images using neural implicit signed distance functions shows impressive performance on 3D Reconstruction of opaque objects. However, existing methods struggle to reconstruct accurate geometry when applied to translucent objects due to the non-negligible bias in their rendering function. To address the inaccuracies in the existing model, we have reparameterized the density function of the neural radiance field by incorporating an estimated constant extinction coefficient. This modification forms the basis of our innovative framework, which is geared towards highfidelity surface reconstruction and the novel-view synthesis of translucent objects. Our framework contains two stages. In the reconstruction stage, we introduce a novel weight function to achieve accurate surface geometry reconstruction. Following the recovery of geometry, the second phase involves learning the distinct scattering properties of the participating media to enhance rendering. A comprehensive dataset, comprising both synthetic and real translucent objects, has been built for conducting extensive experiments. Experiments reveal that our method outperforms existing approaches in terms of reconstruction and novel-view synthesis.

Tailor-Made Design of Three-Dimensional Batteries Using a Simple, Accurate Geometry Optimization Scheme.

In the rapidly evolving Internet of Things (IoT) society, the demand for microbatteries with high areal energy density is surging. As a promising strategy to enhance areal energy density, three-dimensional (3D) batteries have attracted attention. The feature of 3D batteries is the decoupling of the electrode thickness from the ion-transport distance through the modification of the spatial arrangement of the positive and negative electrodes beyond the conventional parallel plates configuration. This allows for the accommodation of a larger amount of active materials without increasing internal resistance. However, identifying the optimal 3D geometry is a complex task, as it depends on printable materials, the resolution of the fabrication equipment, as well as battery usage, which constitutes a multiobjective optimization problem. To overcome this challenge, we propose a novel approach to determine the optimal 3D microbattery geometry. Our innovative method involves a 3D battery optimization system, which integrates an automatic geometry generator with a quick and accurate performance simulator. This approach allows, for the first time, the discovery of material- and discharge-current-dependent optimal geometries. We successfully apply this optimization scheme to two standard electrode pairs (LiFePO4/Li4Ti5O12 and LiNi0.5Mn0.3Co0.2O2/graphite), demonstrating a significant increase in energy density (30%-40% greater than the current state-of-the-art geometry), particularly under high current conditions. These findings underscore the importance of tailor-made batteries for diverse IoT applications and showcase the potential of our approach in realizing such designs.

A high-order fully Lagrangian particle level-set method for dynamic surfaces

We present a fully Lagrangian particle level-set method based on high-order polynomial regression. This enables meshfree simulations of dynamic surfaces, relaxing the need for particle-mesh interpolation. Instead, we perform level-set redistancing directly on irregularly distributed particles by polynomial regression in a Newton-Lagrange basis on a set of unisolvent nodes. We demonstrate that the resulting particle closest-point (PCP) redistancing achieves high-order accuracy for 2D and 3D geometries discretized on irregular particle distributions and has better robustness against particle distortion than regression in a monomial basis. Further, we show convergence in classic level-set benchmark cases involving ill-conditioned particle distributions, and we present an example application to multi-phase flow problems involving oscillating and dividing droplets.

Spectral Fittings of Warm Coronal Radiation with High Seed Photon Temperature: Apparent Low-temperature and Flat Soft Excess in AGNs

A warm corona has been widely proposed to explain the soft excess (SE) in X-ray above the 2–10 keV power law extrapolation in Active Galactic Nuclei (AGNs). In actual spectral fittings, the warm coronal seed photon temperature (T s) is usually assumed to be far away from the soft X-ray, but kT s can reach close to 0.1 keV in the standard accretion disk model. In this study, we used Monte Carlo simulations to obtain radiation spectra from a slab-like warm corona and fitted the spectra using the spherical-geometry-based routine thcomp or a thermal component. Our findings reveal that high T s can influence the fitting results. A moderately high kT s (around 0.03 keV) can result in an apparent low-temperature and flat SE, while an extremely high kT s (around 0.07 keV) can even produce an unobserved blackbody-like SE. Our conclusions indicate that, for spectral fittings of the warm coronal radiation (SE in AGNs), kT s should be treated as a free parameter with an upper limit, and an accurate coronal geometry is necessary when kT s > 0.01 keV.

Image-based non-isotropic point light source calibration using digital fringe projection

Accurate light source calibration is critical in numerous applications including physics-based computer vision and graphics. We propose an image-based method for calibrating non-isotropic point light sources, addressing challenges posed by their non-radially symmetric radiant intensity distribution and the non-Lambertian properties of the calibration target. We deduce an image formation model, and capture the intensity of the calibration target at multiple poses, coupled with accurate 3D geometry acquisition using the digital fringe projection technique. Finally, we design an iterative computational framework to optimize all the light source parameters simultaneously. The experiment demonstrated the high accuracy of the calibrated light source model and showcased its application by estimating the relative reflectance of diverse surfaces.

Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach

Selective laser melting (SLM) can produce complex metal components with high densities, thereby surpassing the limitations of traditional machining methods. However, achieving accurate dimensions, geometries, and acceptable surface states in parts fabricated through SLM remains a concern as they often fall short compared to traditionally machined components. As a solution, a hybrid additive-subtractive manufacturing (HASM) method was developed to effectively utilize the advantages of both techniques. In this study, SLM-made 316 L stainless steel was machined under distinct cooling conditions to investigate the effects of roughness and tool wear. After a thorough investigation, the dual-MQL strategy was evaluated and compared with dry and MQL cutting strategies. The findings showed that the dual-MQL condition led to a significant reduction in flank wear by 54–56% and 29–34%, respectively, associated with dry and MQL cutting techniques, making it a highly promising key for machining SLM-made steel components. Machine learning techniques are potential tools for prediction and classification capabilities in machining processes. For milling SLM-made 316 L SS, multilayer perceptron (MLP) proved to be the most effective prediction model and for classification MLP and Random forest performed better.

Efficient data acquisition and reconstruction for air-coupled ultrasonic robotic NDE

Non-destructive evaluation of complex parts using surface scanning techniques, such as ultrasonic testing and eddy current testing, requires complex manipulation of such sensors to ensure quantitative results. A robotic arm may function as a complex manipulator for surface scanning, controlling the position and tilt between the probe and specimen’s surface. To ensure accuracy in probe manipulation, accurate geometric information of the specimen is required. This article explores a methodology that uses structured light for physical-to-virtual reconstruction, providing submillimeter scale and accurate surface geometries. Reconstruction aids in path planning through a novel ray-triangle intersection array algorithm, establishing movements for the NDE probe to orient itself on the specimen at a constant probe to specimen surface distance, or lift-off. The proposed technique is demonstrated and validated through experimental air-coupled ultrasonic inspection of automotive CFRP composite samples with simulated flaws such as interlaminar delamination. The proposed method employs guided waves and a pitch-catch configuration of air-coupled ultrasonic probes, enabling single-side access scans. A Fanuc 100ib robot arm was used to manipulate the ultrasonic probes along a sample reconstructed with a CR-Scan 01 structured light sensor. The probes were excited at 200khz from a SonoAir system, while also recovering defect vs background information synchronized with the probe’s orientation. Additionally, a framework for potential automation is proposed, with further details to be explored in future works.

Development of bone surrogates by material extrusion-based additive manufacturing to mimic flexural mechanical behaviour and fracture prediction via phase-field approach

Background and objectiveThe limited availability of human bone samples for investigation leads to the demand for alternatives. Bone surrogates are crucial in promoting research on the intricate mechanics of osseous tissue. However, solutions are restricted to commercial brands, which frequently fail to faithfully replicate the mechanical response of bone, or oversimplified customised simulants designed for a specific application. The manufacturing and assessment of reliable bone surrogates made of polylactic acid via material extrusion-based additive manufacturing are presented in this work. MethodsAn experimental and numerical study with 3D-printed dog-bone and prismatic specimens was carried out to characterise the polymeric feedstock and analyse the influence of process parameters under three-point bending and quasi-static conditions. Besides, three porcine rib samples were considered as a reference for the development of the artificial bones. Bone surrogates were manufactured from the 3D-scanned real bone geometries. In order to reproduce the trabecular and cortical bone, a lattice structure for the infill and a compact shell surrounding the core were employed. Infill density and shell thickness were evaluated through different printing configurations. Additionally, a computational analysis based on the phase-field approach was conducted to simulate the experimental tests and predict fracture. The modelling considered homogenisation of the infill material. ResultsOutcomes demonstrated the potential of the presented methodology. Maximum force and flexural stiffness were compared to real bone properties to find the optimal printing configuration, replicating the flexural mechanical behaviour of bone tissue. Certain configurations accurately reproduce the studied properties. Regarding the numerical model, strength and stiffness prediction was validated with experimental results. ConclusionsThe presented methodology enables the manufacturing of artificial bones with accurate geometries and tailored mechanical properties. Furthermore, the described modelling strategy offers a powerful tool for designing bone surrogates.

Recent advances and research progress on the role of carbon‐based biomass in ultra‐capacitors: A systematic review

AbstractBiomass‐derived carbon material has drawn significant attention recently due to its wide availability, environmentally free, and effective performance of the resulting porous carbons for supercapacitor (SC) applications. Carbon electrode material derived from biomass is used for energy storage (ES) because it has distinct qualities in porosity, a large specific surface area, and excellent conductivity. Furthermore, these materials' homogeneous, flawless biological structures can be used as models to create electrode materials with accurate geometries. The ES devices, known as SCs, also known as ultra‐capacitors, serve as a link between a capacitor and a battery. Due to their charge storage, SCs can produce a much higher density than batteries. Several factors, including the electrode's potential window, the electrode materials characteristics, and the electrolyte choice, have a major effect on SC performance. Therefore, all efforts have been made to develop SC electrode materials. This paper explains the different types of SCs and how they work. The various available biomass resources, as well as the methods for producing them, are outlined. In addition, the different types of electrode materials, activation methods, heteroatom functionalization, and electrolyte types are all thoroughly examined. The application and research advancement of biomass‐derived carbon used in SCs over the past 3 years are highlighted. Furthermore, this research outlines the benefits of SCs for the environment and the economy, as well as present challenges and future recommendations for advancing biomass‐derived carbon applications. This article aims to give an in‐depth knowledge of carbon‐based biomass materials that are used in SCs.

Experimental validation of an inverse method for defect reconstruction in a two-dimensional waveguide model.

Defect reconstruction is essential in non-destructive testing and structural health monitoring with guided ultrasonic waves. This paper presents an algorithm for reconstructing notches in steel plates, which can be seen as artificial defects representing cracks by comparing measured results with those from a simulation model. The model contains a parameterized notch, and its geometrical parameters are to be reconstructed. While the algorithm is formulated and presented in a general notation, a special case of guided wave propagation is used to investigate one of the simplest possible simulation models that discretizes only the cross section of the steel plate. An efficient simulation model of the plate cross section is obtained by the semi-analytical scaled boundary finite element method. The reconstruction algorithm applied is gradient-based, and algorithmic differentiation calculates the gradient. The dedicated experimental setup excites nearly plane wave fronts propagating orthogonal to the notch. A scanning laser Doppler vibrometer records the velocity field at certain points on the plate surface as input to the reconstruction algorithm. Using two plates with notches of different depths, it is demonstrated that accurate geometry reconstruction is possible.

Image quality evaluation of a new high-performance ring-gantry cone-beam computed tomography imager

Objective. Newer cone-beam computed tomography (CBCT) imaging systems offer reconstruction algorithms including metal artifact reduction (MAR) and extended field-of-view (eFoV) techniques to improve image quality. In this study a new CBCT imager, the new Varian HyperSight CBCT, is compared to fan-beam CT and two CBCT imagers installed in a ring-gantry and C-arm linear accelerator, respectively. Approach. The image quality was assessed for HyperSight CBCT which uses new hardware, including a large-size flat panel detector, and improved image reconstruction algorithms. The decrease of metal artifacts was quantified (structural similarity index measure (SSIM) and root-mean-squared error (RMSE)) when applying MAR reconstruction and iterative reconstruction for a dental and spine region using a head-and-neck phantom. The geometry and CT number accuracy of the eFoV reconstruction was evaluated outside the standard field-of-view (sFoV) on a large 3D-printed chest phantom. Phantom size dependency of CT numbers was evaluated on three cylindrical phantoms of increasing diameter. Signal-to-noise and contrast-to-noise were quantified on an abdominal phantom. Main results. In phantoms with streak artifacts, MAR showed comparable results for HyperSight CBCT and CT, with MAR increasing the SSIM (0.97–0.99) and decreasing the RMSE (62–55 HU) compared to iterative reconstruction without MAR. In addition, HyperSight CBCT showed better geometrical accuracy in the eFoV than CT (Jaccard Conformity Index increase of 0.02–0.03). However, the CT number accuracy outside the sFoV was lower than for CT. The maximum CT number variation between different phantom sizes was lower for the HyperSight CBCT imager (∼100 HU) compared to the two other CBCT imagers (∼200 HU), but not fully comparable to CT (∼50 HU). Significance. This study demonstrated the imaging performance of the new HyperSight CBCT imager and the potential of applying this CBCT system in more advanced scenarios by comparing the quality against fan-beam CT.

REVIEW OF THE USE OF DRONES AND NON-METRIC CAMERAS FOR THE PROVISION OF LARGE-SCALE GEOSPATIAL DATA ACCORDING TO BIG REGULATION NO. 1 OF 2020

Currently, the need for large-scale mapping for the entire territory of Indonesia is urgent. Therefore, accelerating the provision of large-scale Geospatial Data (DG) is essential for better spatial planning and regional development. The use of drone technology with non-metric cameras is starting to be used for the provision of large-scale DG. To regulate the use of drones and non-metric cameras, the Geospatial Information Agency issued the Head of Geospatial Information Agency Regulation No. 1 of 2020. The purpose of this paper will review the use of drones with non-metric cameras that have been regulated in the agency's regulations. The method used in this paper uses qualitative research with a data collection strategy or source using the literature method. The results show that the use of direct georeferencing in BIG Regulation No. 1 of 2020 has fulfilled the horizontal and vertical geometry accuracy requirements stipulated in BIG Head Regulation No. 6 of 2018 on Base Map Accuracy. The GSD value requirement in BIG Regulation No 1/2020 is too high compared to the GSD value requirement specified in the ASPRS Accuracy Standards for Digital Geospatial Data. This agency regulation is a standard / reference that must be met for all mapping industry players. Therefore, the implementation of this agency regulation requires further study to truly support the issue of accelerating large-scale mapping.

The Effect of the Deposition Strategy and Heat Treatment on Cold Spray Additive Manufactured 316L Stainless Steel

Herein, the effect of heat treatment on the characteristics and properties of cold spray additive manufactured 316L stainless steel employing traditional and a new metal knitting strategy is investigated. 316L feedstock powder characteristics, the geometry of the bulk, microstructure, porosity, microhardness, mechanical isotropy, and residual stress are analyzed in both strategies in as‐sprayed and heat‐treated conditions. Results show that the traditional deposition strategy produced higher mechanical resistance, whereas metal knitting presents a better part geometry accuracy. The heat treatment significantly improves the material strength and the quality of the parts by recovery and recrystallization phenomena. The same microhardness and planar isotropy are achieved after heat treatment of samples produced by both strategies. A discussion about the mechanisms, microstructural, and residual stress evolution is presented.

Analysis of Aortic Arch Hemodynamics With Simulated Bird's Beak Effects.

The objective of this study was to investigate the flow effects in different degrees of thoracic aortic stent graft protrusion extension by creating bird beak effect simulations using accurate 3D geometry and a realistic, nonlinear, elastic biomechanical model using computer-aided software SolidWorks. Segmentation in 3D of an aortic arch from a computed tomography (CT) scan of a real-life patient was performed using SolidWorks. A parametric analysis of three models was performed: (A) Aortic arch with no stent, (B) 3mm bird-beak configuration, and (C) 6.5mm bird-beak configuration. Flow velocity, pressure, vorticity, wall shear stress (WSS), and time average WSS were assessed. The flow velocity in Model A remained relatively constant and low in the area of the ostium of the brachiocephalic artery and doubled in the left subclavian artery. On the contrary, Models B and C showed a decrease in velocity of 52.3 % in the left subclavian artery. Furthermore, Model B showed a drop in velocity of 82.7% below the bird-beak area, whereas Model C showed a decline of 80.9% in this area. The pressure inside the supra-aortic branches was higher in Model B and C compared with Model A. In Model A, vorticity only appeared at the level of the descending aorta, with low to non-vorticity in the aortic arch. In contrast, Models B and C had an average vorticity of 241.4Hz within the bird beak area. Regarding WSS, Model A, and Model B shared similar WSS in the peak systolic phase, in the aortic arch, and the bird beak area, whereas Model C had an increased WSS by 5Pa on average at these zones. In the present simulations' lower velocities, higher pressures, vortices, and WSS were observed around the bird beak zone, the aortic arch, and the supra-aortic vessels.

Assessing the accuracy of TD-DFT excited-state geometries through optimal tuning with GW energy levels.

We study the accuracy of excited state (ES) geometries using optimally tuned LC-PBE functionals with tuning based on GW quasiparticle energies. We compare the results obtained with the PBE, PBE0, non-tuned, and tuned LC-PBE functionals with available high-level CC reference values as well as experimental data. First, we compare ES geometrical parameters obtained for three different types of systems: molecules composed of a few atoms, 4-(dimethylamino)benzonitrile (DMABN), and conjugated dyes. To this end, we used wave-function results as benchmarks. Next, we evaluate the accuracy of the theoretically simulated spectra as compared to the experimental ones for five large dyes. Our results show that, besides small compact molecules for which tuning LC-PBE does not allow obtaining geometries more accurate than those computed with standard functionals, tuned range-separated functionals are clearly to be favored, not only for ES geometries but also for 0-0 energies, band shapes, and intensities for absorption and emission spectra. In particular, the results indicate that GW-tuned LC-PBE functionals provide improved matching with experimental spectra as compared to conventionally tuned functionals. It is an open question whether TD-DFT with GW-tuned functionals can qualitatively mimic the actual many-body Bethe-Salpeter (BSE/GW) formalism for which analytic ionic gradients remain to be developed.

Binder Jetting 3D Printing of Binary Cement-Siliceous Sand Mixture.

Three-dimensional printing allows accurate geometries to be obtained across a wide range of applications and it is now also moving into the architecture and construction industry. In the present work, a unique binary mix composed of ordinary Portland cement (OPC) and quick-setting cement (QSC) was combined with silica sand aggregate in different proportions for a customized binder jetting 3D printing (BJ3DP) process. Specimens were printed using the blended dry powders and deionized water to determine the impact of the processing variables on the properties of the realized specimens. The results show that the properties are influenced by the binary mix proportions and the layer thickness. The investigation found significant improvement in mechanical performance on increasing the proportion of OPC and optimal conditions were identified with proportions of 35 wt% OPC and 5 wt% QSC. Notable enhancements were also observed as the layer thickness was reduced.

Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation

A crucial reason for the success of existing NeRF-based methods is to build a neural density field for the geometry representation via multiple perceptron layers (MLPs). MLPs are continuous functions, however, real geometry or density field is frequently discontinuous at the interface between the air and the surface. Such a contrary brings the problem of unfaithful geometry representation. To this end, this paper proposes spiking NeRF, which leverages spiking neurons and a hybrid Artificial Neural Network (ANN)-Spiking Neural Network (SNN) framework to build a discontinuous density field for faithful geometry representation. Specifically, we first demonstrate the reason why continuous density fields will bring inaccuracy. Then, we propose to use the spiking neurons to build a discontinuous density field. We conduct a comprehensive analysis for the problem of existing spiking neuron models and then provide the numerical relationship between the parameter of the spiking neuron and the theoretical accuracy of geometry. Based on this, we propose a bounded spiking neuron to build the discontinuous density field. Our method achieves SOTA performance. The source code and the supplementary material are available at https://github.com/liaozhanfeng/Spiking-NeRF.

Enhancing the geometry accuracy and mechanical properties of wire arc additive manufacturing of an Al-5%Mg alloy through longitudinal magnetic field influence

To address the issues of low deposition efficiency, poor geometric accuracy, and high porosity in wire arc additive manufacturing (WAAM) specimens, a longitudinal alternating current (AC) magnetic field-assisted WAAM method was employed. The results showed that the introduction of the longitudinal AC magnetic field significantly enhanced the geometric accuracy of the specimens. After magnetic field assistance, the porosity of the WAAM Al-5%Mg alloy specimens decreased from 0.372 % to 0.016 %. The grain size decreases from 71.95 μm to 56.3 μm, with a notable increase in the proportion of small grains (D < 30 μm) from 27.06 % to 44.57 %. The longitudinal tensile strength increased from 243 MPa to 260 MPa, while the transverse tensile strength increased from 232 MPa to 257.7 MPa. Moreover, the longitudinal and transverse tensile strengths tended to be isotropic. Therefore, the longitudinal AC magnetic field has a significant impact on the performance of AlMg alloy WAAM specimens. This study presents novel strategies and theoretical guidance for enhancing the efficiency and component performance of aluminum alloy WAAM technology.