Complex Geometrical Features Research Articles

Gaze tracking embedded collaborative robots for automated metrology and reverse engineering

Conventional geometric metrology, or three-dimensional (3D) scanning, and reverse engineering heavily rely on the experience of the operators. With an increasing need for automation, robot arms have been adopted for this task. However, due to the large variety of parts and designs, automated path planning could provide a scanning solution that may overlook the critical area, which could potentially deteriorate the scan results. This article explores the integration of collaborative robotics (cobots) with eye-tracking technology to improve the autonomous 3D scanning process. The primary objective of this study is to enhance the accuracy and efficiency of cobots in 3D scanning, particularly in the capture of functionally critical areas, and to provide a detailed description of regions with complex geometric features. The study develops a framework where the scanning path of the robot-driven scanner is partially guided by eye tracking data, that is, calibrated gaze tracking, to improve the automated 3D scanning process. This framework provides an innovative integration of human gaze movement with automatic robot path planning, providing a new way of human-autonomy teaming. Case studies are presented to present and validate the proposed framework to automatically improve the 3D point cloud collection process, specifically in areas that usually require human manual intervention to capture details.

Experimental evaluation of novel gas liquid contactor packing based on triply periodic minimal surfaces (TPMS)

Additive manufacturing has widened the possibilities of design for gas–liquid packing geometries in recent years, allowing for new unexplored shapes. A novel design approach based on triply periodic minimal surfaces (TPMS11Triply periodic minimal surfaces.), an interesting class of smoothened curved surfaces, led to the conception of new periodic structures with complex geometrical features. The present study aims to assess the applicability of these new TPMS-based geometries as packing for gas–liquid contactors and the effect of their topology on performance. Liquid holdup, pressure drop, flooding and mass transfer efficiency were evaluated for three TPMS-based prototypes, with distinct characteristic dimensions. A new experimental setup was developed for the estimation of liquid holdup based on the measure of the liquid weight during operation by a set of load cells. Results show good repeatability and precision, allowing to distinguish liquid holdup between the prototypes for various gas and liquid flow rates. Two of the studied prototypes presented higher holdup than the 5 ppi ceramic foam and the 15 mm Raschig rings for lower liquid rates. Prototypes also showed good results for pressure drop, with flooding reached at gas load factors above 3Pa0.5. In terms of mass transfer efficiency, HETP results for the new prototypes remained between 0.35m and 0.45m, comparable to Mellapak 250Y.

Dynamic rupture modeling in a complex fault zone with distributed and localized damage

Active fault zones have complex structural and geometric features that are expected to affect earthquake nucleation, rupture propagation with shear and volumetric deformation, and arrest. Earthquakes, in turn, dynamically activate co-seismic off-fault damage that may be both distributed and localized, affecting fault zone geometry and rheology, and further influencing post-seismic deformation and subsequent earthquake sequences. Understanding this co-evolution of fault zones and earthquakes is a fundamental challenge in computational rupture dynamics with consequential implications for earthquake physics, seismic hazard and risk. Here, we implement a continuum damage-breakage (CDB) rheology model in our MOOSE-FARMS dynamic rupture simulator to investigate the interplay between bulk damage and fault motion on the evolution of dynamic rupture, energy partitioning, and ground motion characteristics. We demonstrate several effects of damage (accounting for distributed cracking) and breakage (accounting for granulation) on rupture dynamics in the context of two prototype problems addressed currently in the 2D plane-strain setting: (1) a single planar fault and (2) a fracture network. We quantify the spatio-temporal reduction in wave speeds associated with dynamic ruptures in each of these cases and track the evolution of the original fault zone geometry. The results highlight the growth and coalescence of localization bands as well as competition between localized slip on the pre-existing faults vs. inelastic deformation in the bulk. We analyze the differences between off-fault dissipation through damage-breakage vs. plasticity and show that damage-induced softening increases the slip and slip rate, suggesting enhanced energy radiation and reduced energy dissipation. These results have important implications for long-standing problems in earthquake and fault physics as well as near-fault seismic hazard, and they motivate continuing towards 3D simulations and detailed near-fault observations to uncover the processes occurring in earthquake rupture zones.

Application of Artificial Neural Networks to a Model of a Helicopter Rotor Blade for Damage Identification in Realistic Load Conditions.

Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMSs) perform real-time monitoring functions, but their performance must be carefully assessed. This is typically done by introducing artificial damages to the components; however, such a procedure requires the production and testing of a large number of structural elements. In this work, the damage detection performance of a strain-based SHMS was evaluated on a composite helicopter rotor blade root, exploiting a Finite Element (FE) model of the component. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through FE analyses. The effects of the load's variability and of the damage were decoupled by including a load recognition step in the algorithm, which was accomplished either with an Artificial Neural Network (ANN) or a calibration matrix. Anomaly detection, damage assessment, and localization were performed by using an ANN. The results showed a higher load identification and anomaly detection accuracy using an ANN for the load recognition, and the load set was recognized with a satisfactory accuracy, even in damaged blades. This case study was focused on a real-world subcomponent with complex geometrical features and realistic load conditions, which was not investigated in the literature and provided a promising approach to estimate the performance of a strain-based SHMS.

Investigation of complex single-walled intersecting structures fabricated by wire-arc directed energy deposition

PurposeThis study aims to investigate an intersecting single-walled structure fabricated using wire-arc directed energy deposition (waDED). Because of the highly complex geometrical features of this structure, characterisation is used to identify potential weak points and provide a benchmark for future complex components.Design/methodology/approachA structural component with a process-specific design is built using additive manufacturing of an Al-Mg alloy and analysed using micro-computed tomography. Scans are carried out at different resolutions and subsequently compared to microsections. The chemical composition and hardness are also examined. These investigations provide an enhanced understanding of defects and overall quality of the manufactured parts.FindingsThe results show that very high-quality parts can be achieved using ER5183 alloy, even in intersecting areas. Defects in these regions are primarily caused by converging and diverging waDED paths and discontinuous waDED operations.Originality/valueIn addition to demonstrating the feasibility of complex structures using waDED, this study provides an overview of problem areas and potential improvements in waDED manufacturing.

Research on Film Formation Characteristics by Spraying on Unidiameter Vertical Interpenetrating Cylindrical Surfaces

Unidiameter Vertical Interpenetrating Cylindrical Surfaces (UVICS, also called T-pipe surfaces) are a type of typical complex surface that exists in facilities or equipment such as oil storage tanks and industrial pipelines. The shape and surface characteristics of a component undergoing spraying will have a significant impact on the spray flow field and the resulting coating film. In order to optimize the coating effects of complex surfaces, the Euler-Euler approach was utilized to model a spray film formation process that encompasses both a spray flow field model and a wall adhesion model. Subsequently, the influence of the geometric features, geometric dimensions, lateral air pressure of the spray gun, and spraying distance on the coating film characteristics of this kind of surface were systematically investigated. It is determined that the film thickness uniformity could be enhanced by decreasing the dimensions of the workpiece or increasing the lateral air pressure and spraying distance in an appropriate manner when spraying at the location with the most complex geometric features of UVICS. Furthermore, the optimal parameters under varying spraying conditions were identified. The experiments validated the accuracy of the numerical simulation results and demonstrated the feasibility of this simulation model. The study is of significant value in addressing the challenges associated with film formation during spraying on complex surfaces, developing a comprehensive theoretical framework for air spraying, and expanding the scope of applications for automatic spraying technology.

Electroplating Additively Manufactured Honeycomb Structures to Increase Energy Absorption Under Quasi-Static Crush

Honeycomb (HC) has been used in energy absorption applications due to its high stiffness and low density. Metallic HC are used for energy absorption applications, however, these metallic structures can be challenging to manufacture if complex geometric features designed to improve energy absorption are used, which motivates the use of additive manufacturing (AM). Metal AM methods include powder bed fusion (PBF) and direct energy deposition (DED). In addition to capital equipment cost, these processes possess challenges that include a required inert environment, powder handling, final part porosity, residual stresses, and nonuniform surface finish. These concerns can be alleviated through the use of polymer AM, however, polymeric parts exhibit brittle failure and have a lower stiffness than metallic HC structures. In this study, a low-cost 3D polymer printing method, stereolithography (SLA), is combined with a conventional electroplating process to fabricate a metal-plastic composite HC structure with energy absorption capability much greater than of a plastic HC structures of the same nominal volume. SLA parts have a smooth surface, so that the surface finish is at least as uniform after electroplating as the SLA part. The energy absorption characteristics of the electroplated HC is studied to determine how these energy absorbing materials can be manufactured at reduced cost. Our study confirms that the metal-plastic composite HC increases both the crush strain range and the mean crush stress of these samples, resulting in metal-plastic composite HC structures with substantially increased energy absorption. This study also examines how buckling initiators (BIs), or diamond shaped holes located at 50, 75, and 100% of the height of the hexagonal cell vertices, can influence energy absorption performance. This study shows that it is feasible to fabricate electroplated HCs, using an SLA preform, to achieve a substantial increase in energy absorption over using SLA alone.

Research on the Reconstruction Method of Old Houses in Mindong, Republic of China, Based on a Wearable Mobile Scanning System

To address the complex structures of historical buildings in Mindong, lack of historical reference materials, and heavy workload of traditional surveying and mapping methods in the field, a 3D reconstruction method of historical building based on a wearable mobile scanning system for data acquisition was studied. First, a wearable mobile scanning system is used to scan historical buildings in three dimensions. The data are fused to obtain high-precision three-dimensional point cloud data of historical buildings. Second, based on the processed point cloud, a 2D map of historical buildings is accurately drawn using point cloud slicing. Finally, three-dimensional reconstruction of historical buildings is realized using a 2D atlas of historical buildings and modern modeling technology. The accuracy of the three-dimensional reconstruction model was analyzed using a fitting algorithm and structural geometric relationship to verify the effectiveness and accuracy of the method. An old house in Mindong, Republic of China, was taken as the research object. Experimental analysis revealed that (1) a wearable mobile scanning system is suitable for high-quality point cloud data acquisition of buildings with complex geometric features, solving the problems of low efficiency and poor coverage of traditional surveying and mapping results; and (2) the method realizes 2D results rendering and 3D reconstruction of historical buildings based on the wearable mobile scanning system data with good accuracy, providing a reference for the protection and repair of historical buildings and health assessment.

A transfer learning approach for data-driven localization of damage areas in plate-like structures of CFRP materials

The application of machine learning techniques based on Lamb waves for structural health monitoring of composite material damage has been widely adopted. However, this method often struggles to acquire sufficient labeled historical Lamb wave signals for training machine learning models. This issue can be addressed through the implementation of transfer learning. This paper proposes a data-driven transfer learning model for locating coordinates of damage areas in plate-like structures with complex geometric features such as rivets and grooves. Based on the differences in conditional distributions in various structural health monitoring scenarios, a Cross-Component Regression for Damage Localization Model (CRDM) is introduced for damage localization across components. Due to the limited availability of labeled target data, CRDM aims to preserve global properties of the target data-dominated conditional distribution, besides considering the predictive accuracy of individual samples. Therefore, a hybrid loss function is constructed by combining Mean Squared Error (MSE) and Conditional Embedding Operator Difference (CEOD). Subsequently, the target model is fine-tuned based on this loss function. Experimental validations are conducted on finite element models and real datasets, confirming the effectiveness of the CRDM model in damage localization for plate-like structures.

A single mesh approximation for modeling multiphase flow in heterogeneous porous media

The control volume finite element (CVFE) approach is based on the discretization of primary flow and transport unknowns on two different meshes. The element mesh, used to represent the physical properties of the medium, differs from the control volume mesh necessary to ensure a mass conservative solution. The inherent two mesh feature of the CVFE approximation introduces inconsistency in the transport solution due to the averaging of physical and computed quantities between neighboring elements in the mesh. In this work, we present a consistent approach for modeling multiphase flow and transport in heterogeneous porous media. The approach is applied in the CVFE framework by enabling the discretization of primary unknowns on a single mesh. We combine the discretization of an element-wise, discontinuous pressure approximation with a first-order, discontinuous velocity approximation to resolve the elliptic or parabolic flow problem. The effectiveness of the formulation is achieved by exploiting the same finite element mesh as the flow problem, when updating the saturation solution. This direct mapping between the flow and transport mesh is simple yet effective for establishing a consistent solution in addition to circumventing the non-physical mass leakage exhibited in the classical CVFE method. We describe the interface approximations and the discontinuous terms needed for consistent solutions. The method is well suited to model flow in complex geometrical subsurface domains and is shown to be numerically stable while providing locally and globally mass conservative solutions. We apply the approach to several domains with complex geometrical features to emphasize the superiority of the approximation over conventional methods. The analysis shows over two orders of magnitude reduction in solution error compared to the classical CVFE. The new formulation effectively captures accurate transport solutions, even in the presence of varying material properties, without the need for mesh refinement.

DeHNSSo: The Delft Harmonic Navier-Stokes Solver for nonlinear stability problems with complex geometric features

A nonlinear Harmonic Navier-Stokes (HNS) framework is introduced for simulating instabilities in laminar spanwise-invariant shear layers, featuring sharp and smooth wall surface protuberances. While such cases play a critical role in the process of laminar-to-turbulent transition, classical stability theory analyses such as parabolized or local stability methods fail to provide (accurate) results, due to their underlying assumptions. The generalized incompressible Navier-Stokes (NS) equations are expanded in perturbed form, using a spanwise and temporal Fourier ansatz for flow perturbations. The resulting equations are discretized using spectral collocation in the wall-normal direction and finite-difference methods in the streamwise direction. The equations are then solved using a direct sparse-matrix solver. The nonlinear mode interaction terms are converged iteratively. The solution implementation makes use of a generalized domain transformation to account for geometrical smooth surface features, such as humps. No-slip conditions can be embedded in the interior domain to account for the presence of sharp surface features such as forward- or backward-facing steps. Common difficulties with Navier-Stokes solvers, such as the treatment of the outflow boundary and convergence of nonlinear terms, are considered in detail. The performance of the developed solver is evaluated against several cases of representative boundary layer instability growth, including linear and nonlinear growth of Tollmien-Schlichting waves in a Blasius boundary layer and stationary crossflow instabilities in a swept flat-plate boundary layer. The latter problem is also treated in the presence of a geometrical smooth hump and a sharp forward-facing step at the wall. HNS simulation results, such as perturbation amplitudes, growth rates, and shape functions, are compared to benchmark flow stability analysis methods such as Parabolized Stability Equations (PSE), Adaptive Harmonic Linearized Navier-Stokes (AHLNS), or Direct Numerical Simulations (DNS). Good agreement is observed in all cases. The HNS solver is subjected to a grid convergence study and a simple performance benchmark, namely memory usage and computational cost. The computational cost is found to be considerably lower than high-fidelity DNS at comparable grid resolutions. Program summaryProgram Title: DeHNSSoCPC Library link to program files:https://doi.org/10.17632/9bnms99kk2.1Developer's repository link:https://github.com/SvenWesterbeek/DeHNSSoLicensing provisions: GPLv3Programming language: MatlabSupplementary material: The supplementary material contains the code as well as a user manual.Nature of problem: Fluid flows are subject to laminar-to-turbulent transition following the growth of instabilities. To avoid computationally demanding Direct Numerical Simulations (DNS), perturbation theory is often applied to their analysis. However, classical stability methods based on the Orr-Sommerfeld equation or the Parabolized Stability Equations neglect the influence of streamwise gradients in varying degrees. The validity of these assumptions is difficult to estimate a priori.Solution method: The Delft Harmonic Navier-Stokes Solver (DeHNSSo) solves the harmonic Navier-Stokes equations nonlinearly on domains featuring sharp and smooth spanwise invariant surface features using a generalized grid approach in combination with an embedded boundary method. This allows the user to include the effects of streamwise gradients on flow at a fraction of the cost of DNS.Additional comments including restrictions and unusual features: In DeHNSSo, the equations are solved using direct matrix solvers. As such, memory is treated as a scarce resource. The problem is formulated in a mode-independent manner such that left-hand side matrices need only be computed and stored once despite incorporating nonlinear terms. Additionally, DeHNSSo offers the user the possibility to prescribe inhomogeneous boundary conditions to introduce and solve the receptivity problem or manipulate instabilities. Due to the double Fourier expansion, the solver is restricted to spanwise and temporally periodic problems.

Matching the building footprints of different vector spatial datasets at a similar scale based on one-class support vector machines

Automatic matching of multisource data is an important technique for achieving change detection, fusion and updating spatial data. However, most current learning methods for building footprint matching require a large number of samples, and labeling these samples is costly in terms of labor and time. Moreover, multisource building footprint data are complex and diverse leading to recognizing the different matching relationships is a hard task. Thus, this study proposes a learning-based method for recognizing multisource building footprints matching relationships by using a one-class support vector machine (OCSVM). The OCSVM was trained using only positive samples. First, a set of geometric indicators was designed to train a model and realize initial matching recognition. Then, a contextual metric was calculated based on the rough matching results, and geometric and contextual metrics were combined to train the model and realize relaxed matching recognition. Relaxed matching is an optimization process implemented after initial matching to recognize more relaxed matching relationships. In relaxed matching, a convex hull is used to recognize matching relationships besides 1:1, such as 1:n, m:1 and m:n. The experimental results showed that the proposed method outperformed indicator-weighted (weighted average) and learning-based matching methods, such as traditional SVMs and decision trees (DTs). The precision scores of the proposed model were 97.1%, 95% and 97.2% for the Wuhan (China), Beijing (China) and Richmond Hill (Canada) datasets, respectively. Furthermore, the proposed model identified the matching relationships of buildings with complex geometric features and high-density spatial distributions.

Design and optimization of 3D-bioprinted cell-laden scaffolds in dynamic culture

Light-based 3D printing enables the fabrication of biological scaffolds with high precision, versatility and biocompatibility, particularly the cell-laden scaffolds with architecturally complex geometric features. However, many bioprinted tissue scaffolds suffer from low cell viability due to insufficient oxygen and nutrient supply, which is heavily influenced by scaffold structure and cultivation conditions. Current practice relies mainly on resource-intensive trial-and-error methods to optimize scaffolds’ structures and cultivation parameters. In this study, we developed a comprehensive multi-physics model integrating fluid dynamics, oxygen mass transfer, cell oxygen consumption, and cell growth processes to capture cell growth behaviors in scaffolds, establishing a robust theoretical foundation for scaffold structure optimization. The modeling results showed that a large number of parameters, such as system inlet flow rate, geometric feature size, cell parameters, and material properties, significantly impact oxygen concentration and cell growth within the scaffold. A two-step optimization strategy is proposed in this paper and was applied to obtain optimal geometric parameters of channeled scaffolds to demonstrate the model’s effectiveness for scaffold optimization. The model can be employed for scaffolds with arbitrary shapes and various materials, facilitating the optimal design of sophisticated scaffolds for more advanced tissue engineering.

LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments

Accurate localization of robots in unstructured environments poses challenges due to low localization accuracy and local trajectory oscillation caused by complex feature points when using Euclidean-based filtering methods. In this study, we propose a novel 3D robot localization method named LIF-M that leverages a manifold-based approach in conjunction with an unscented Kalman filter (UKF-M). Additionally, a relocalization algorithm is designed to ensure localization stability. The proposed method addresses the limitations of conventional Euclidean-based filtering methods by incorporating manifold-based techniques, providing a more comprehensive representation of the complex geometric features. We introduce the manifold concept, where the relevant definition is defined and utilized within the LIF-M framework. By combining left and right invariants, we effectively reduce noise uncertainty, resulting in improved localization accuracy. Moreover, we employ sigma points as a matrix representation of the state points’ space in order to seamlessly transition between the matrix space and the vector representation of the tangent space. Experimental tests and error calculations were conducted to evaluate the performance of various algorithm frameworks, and the results demonstrated the importance of the manifold-based approach for accurate attitude estimation. Compared to the standard UKF, the manifold space equips LIF-M with better robustness and stability in unstructured environments.

Reducing Surface Roughness of Selective Laser Melting of 316 Stainless Steel Component by Electropolishing

Additive manufacturing (AM) is widely used in the fields of aerospace, automotive, and biomedical. Because it enables to fabricate and customize parts with complex geometrical features. However, due to the layer-by-layer deposition and the partially fused raw material, AM part suffers from the step and balling effects, which shows inadequate and poor surface quality. Conventional polishing methods such as grinding, milling, and sandblasting have been applied to meet the requirement of well surface quality. But these contact polishing techniques usually introduce extra residual stress and surface damage, which are unsuitable for workpieces with complex shapes. In this study, we propose an environmentally friendly electrochemical polishing method for AM 316L stainless steel parts based on a combination of positive and negative pulses. Unlike the traditional phosphoric acid or sulfuric acid electrochemical polishing liquid system, the sodium chloride electrolyte adopted in this study avoids the pollution problem of waste liquid. A negative pulse was introduced to avoid the impaction of Cl- ions to the passivation film generated during the electrochemical polishing process. The effects of voltage, polishing duration, temperature, and duty cycle were analyzed by orthogonal experiments. Under the optimum reaction conditions, a surface roughness Ra of 0.421μm without pitting corrosion is achieved and the balling effect was eliminated.

A novel knee-guided algorithm based on frequency analysis for non-cyclic dynamic multiobjective optimization problems

In real life, many dynamic multiobjective optimization problems (DMOPs) have both time-varying and non-cyclic properties. Their non-cyclic nature differentiates the dynamics of the problem from all previous moments, and the changes become increasingly complex over time. Therefore, geometric changes in the Pareto-optimal front (PF) are more difficult to track. A novel knee-guided algorithm based on frequency analysis (NKGFA) is designed to solve non-cyclic DMOPs. To identify sufficient feature points to describe the change in PF, a knee-guided prediction strategy based on frequency analysis is proposed. From the perspective of frequency-domain analysis, almost all concave and convex knee points that contain critical feature information on the PF are identified. By predicting the frequency-domain information of the knees, the population’s evolutionary trend is obtained in the frequency-domain space. An interpolation strategy based on approximate frontier manifold construction is proposed to ensure that the population is uniformly distributed on the PF. This strategy established an approximate PF between concave and convex knee points. Moreover, it adaptively interpolated the new individuals in the approximate PF. A non-cyclic dynamic (NCD) benchmark test suite is designed, including PFs with increasingly complex geometric features. The effectiveness of the NKGFA is verified by comparing it with four algorithms in the NCD test suite. The NKGFA performed best on 26, 23, and 29 of the 39 test problems on Inverted Generational Distance (IGD), Schott’s Spacing Metric (SP), and Hypervolume Difference (HVD), respectively.

Boundary representation compatible feature recognition for manufacturing CAD models

This paper presents a Boundary Representation (BREP) compatible data representation for Graph Neural Network (GNN) based feature identification. This data representation follows BREP and STEP AP 203 standards and can transfer holistic manufacturing CAD information to the deep neural network, which assists to identify small local geometrical features and highly complex interactive geometric features. Inversely, this data representation can be easily converted back to a conventional CAD model due to its direct encoding approach. With this data representation, the GNN can reach 99.57% accuracy on 36 classes FeatureNet + dataset and realize 99.12% accuracy on Machining Process Identification (MPI) dataset with highly interactive features. This method can largely benefit Computer Assisted Process Planning and pave the way for future industrial automation.

Geometry‐guided semantic segmentation for post‐earthquake buildings using optical remote sensing images

AbstractDeep‐learning‐based automatic recognition of post‐earthquake damage for urban buildings is increasingly in demand for rapid and precise assessment of seismic hazards from optical remote sensing images. In this study, a novel loss function fusing geometric consistency constraint (GCC) with cross‐entropy (CE) loss is designed for post‐earthquake building segmentation with complex geometric features across multiple scales. Specifically, the GCC loss incorporates three critical components, namely, split line length, curvature, and area, and enables the exact extraction of the geometric constraints of boundary and region for damaged buildings. Through the optimization of multiple key coefficients of GCC loss, the proposed method achieves significant performance improvements in semantic segmentation, which is attributed to the enhanced ability to identify and capture the pixel relationship near the boundary. Merging GCC in the loss function enables faster and more accurate convergence of predicted values towards the ground truth during the training process, surpassing the performance of the CE loss alone. The results show that the combination of GCC and CE losses achieves the largest validation mIoU of 86.98% for damaged buildings segmentation, which facilitates post‐earthquake assessment with high accuracy. Moreover, incorporating GCC leads to more precise and robust seismic damage segmentation by effectively improving edge closure, removing internal noise, and reducing false‐positive and false‐negative misrecognition. In addition, the GCC term further validates the effectiveness of improving segmentation tasks for other networks (e.g., DeepLabv3+). The GCC‐derived method exhibits its desirable performance on segmentation accuracy, portability, and universality for building recognition with complex geometric features and post‐earthquake scenes.

Growth and transtensional reactivation of a basin-bounding fault: Chengbei Sag, Bohai Bay Basin, East China

Models of extensional fault systems are predominantly based on examples where extension is approximately orthogonal to rift boundaries and deformation can be regarded as plane strain. In contrast, extensional faults in transtensional or strike-slip settings present complex geometric and kinematic features due to rotational deformation, and distinct rift and post-rift phases may be difficult to separate. Well-constrained 3D seismic data from the Bohai Bay Basin, East China reveal the growth history and transtensional reactivation of a basin-bounding fault (Chengbei Fault). During the syn-rift phase, the Chengbei Fault reached its near-final length within ∼24 Ma via the propagation and linkage of six initial fault segments, accompanied by the accumulation of 26% of its total displacement; in the following ∼18.7 Ma of the Paleogene, it accumulated the remaining 74% of displacement without an obvious increase in fault length. Following a phase of relative quiescence in the early Neogene, the Chengbei Fault was reactivated at ∼12 Ma, associated with the enhanced activity of a continental-scale strike-slip fault. At least 17 fault segments developed quickly and linked vertically to the lower fault system of the Chengbei Fault. The plan-view en-echelon and horse-tail configuration, and the flower structure seen in section views of the fault system, indicate that the post-rift activity of Chengbei Fault has had both dip-slip and strike-slip components. A calculation of the rotational deformation using fault heaves and orientations indicates that the tectonic reactivation occurred in a strike-slip-dominated transtensional setting accompanied by an increasing extension component with time. In basins affected by strike slip, kinematic analyses that consider rotational deformation can provide more detailed and convincing information on the tectonic setting.

Investigating Complex Geometrical Features in LPBF-Produced Parts: A Material-Based Comparison Between Different Titanium Alloys

The Ti–6Al–4V (Ti64) alloy is a well-established material to be processed via laser powder bed fusion (LPBF). Recently, other α + β titanium alloys are receiving attention, such as Ti–6Al–2Sn–4Zr–6Mo (Ti6246). Their typical industrial fields of application (aerospace, automotive), often require critical design choices, such as low wall thicknesses and hollow channels. Thus, a comparative analysis between these two competitor alloys in terms of processability was conducted in this work. To do so, specific sample designs were developed. The specimens were analyzed in terms of geometrical compliance with the initial design, porosity, and microstructure. A correlation between the width of the specimens and their porosity, microstructure and hardness was found. Overall, both the alloys proved to be well processable, even for very low wall thickness (300 μm) and channel diameter (1 mm) values. Nevertheless, the Ti6246 alloy seemed to behave better in specific scenarios. For instance, some Ti64 specimens provided delamination. The hollow channels proved to be challenging for both materials, mainly due to the high amount of residual powder particles adhered to the upper part of the holes. This works aims at giving a materials perspective on process-related issues, considering the LPBF-induced defectology and microstructural variations in these Ti alloys.Graphical