High Geometrical Complexity Research Articles

Pure copper extrusion additive manufacturing of lattice structures for enabling enhanced thermal efficiency in hydrogen production

The high geometrical complexity allowed by Additive Manufacturing (AM) of pure copper can support the development of more efficient catalytic supports that can enable the design of efficient reactors for key processes for energy transition, i.e., hydrogen production with methane steam reforming or CO2 conversion to synthetic methane, thanks to the enhanced thermal conductivity of the catalytic support. Low relative density lattice geometries with open cell copper geometries can be adopted as catalyst supports in the new generation chemical reactors to improve the heat transfer. This work presents a manufacturability study about pure copper lattice filters produced via metal Extrusion AM i.e., via feedstock 3D printing and sintering. The results show that feasible parts design can be reached and produced and that heat-exchange performances can be increased with respect to conventional supports, as confirmed by lab-scale heat transfer tests. A specific metrological assessment based on CTscan data is implemented on the printed parts to qualify them and to ensure a good thermal coupling between the printed filters and the heat exchanger main tube elements.

Binder System Composition on the Rheological and Magnetic Properties of Nd-Fe-B Feedstocks for Metal Injection Molding

The applications of Nd-Fe-B-based magnets are experiencing significant diversification to achieve efficiency and miniaturization in different technologies. Metal injection molding (MIM) provides new opportunities to manufacture Nd-Fe-B magnets with high geometrical complexity efficiently. In this study, the impacts of the binder system composition and powder loading on the rheological behavior, contamination, and magnetic properties of the Nd-Fe-B MIM parts were investigated. A high-pressure capillary rheometer was used to measure the apparent viscosity and pressure drops for feedstocks with different binder formulations and powder contents. Also, oxygen and carbon contamination, density, and magnetic properties were measured for different feedstock formulations and powder loadings. From the rheological, density, and magnetic properties points of view, the binder system consisting of 45 vol.% LLDPE as backbone was selected as the optimum formulation. The findings indicated that the sample with this binder system and 55 vol.% powder content had a high density (6.83 g/cm3), remanence (0.591 T), and coercivity (744.6 kA/m) compared to other binder compositions. By using 58 vol.% powder loading, the values of density (7.54 g/cm3), remanence (0.618 T), and carbon residue (982 ppm) improved, and a suitable rheological behavior was still observed. Thus, a suitable feedstock formulation was developed.

HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data

BackgroundAdvances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational tools and approaches for integration of transcriptomics data and spatial context information are urgently needed to comprehensively explore the underlying structure patterns. In this manuscript, we propose HyperGCN for the integrative analysis of gene expression and spatial information profiled from the same tissue. HyperGCN enables data visualization and clustering, and facilitates downstream analysis, including domain segmentation, the characterization of marker genes for the specific domain structure and GO enrichment analysis.ResultsExtensive experiments are implemented on four real datasets from different tissues (including human dorsolateral prefrontal cortex, human positive breast tumors, mouse brain, mouse olfactory bulb tissue and Zabrafish melanoma) and technologies (including 10X visium, osmFISH, seqFISH+, 10X Xenium and Stereo-seq) with different spatial resolutions. The results show that HyperGCN achieves superior clustering performance and produces good domain segmentation effects while identifies biologically meaningful spatial expression patterns. This study provides a flexible framework to analyze spatial transcriptomics data with high geometric complexity.ConclusionsHyperGCN is an unsupervised method based on hypergraph induced graph convolutional network, where it assumes that there existed disjoint tissues with high geometric complexity, and models the semantic relationship of cells through hypergraph, which better tackles the high-order interactions of cells and levels of noise in spatial transcriptomics data.

Identifying imaging challenges for 1D and 3D geometric complexity (LG space)

In some situations, it is difficult to image seismic data even when factors such as illumination, anisotropy, and attenuation are resolvable. An often-overlooked but dominant factor affecting imaging is the geometric complexity of subsurface reflectors. In some of these settings, we propose that geometric complexity plays a dominant role in the quality of seismic imaging. This has been documented in subsalt regions with poor image quality where 3D vertical seismic profiles (VSPs) are available. VSPs often demonstrate that there is good illumination below salt, but the complexity of the observed downgoing wave and diffractions cannot be simulated with smooth earth models. We use synthetic examples with high geometric complexity and no other complications to analyze the imaging process: a 2D sediment-salt model with rough salt-top topography, and a 1D model with complex reflectivity. In these models, it is difficult to estimate velocity, and it is challenging to image the data. These synthetic examples are useful to understand the limitations of imaging with smooth models, to create guidelines to identify geometric complexity in field data, and to develop possible mitigations to improve imaging. Further real data examples will be needed to test geometric complexity.

Novel Aluminum Alloy Tailored for Additive Manufacturing: Structural Characterization and Qualification Perspectives

The recent advances achieved in additive manufacturing (AM) technology demonstrate the potential to realize customized metal components, ensuring weight reduction opportunities. These benefits make AM attractive for high-cost aerospace applications, especially where high geometric complexity is required. In the context of an EU research scenario, the H2020 MANUELA (Additive Manufacturing Using Metal Pilot Line) project promotes the development of new technologies for design optimization by enabling the application of novel materials in AM. This paper illustrates recent advances in a new aluminum alloy (Al-HS1) with high strength emphasizing all of the characterization steps at the coupon level. This material has been employed in the re-engineering of a conventional hydraulic manifold using a powder bed fusion-laser beam (PBF-LB) process. Both the simulations and structural tests allowed for proving its compliance and technological maturity with industrial standards and applicable airworthiness requirements.

Meta-Meshing and Triangulating Lattice Structures at a Large Scale

Lattice structures have been widely used in applications due to their superior mechanical properties. To fabricate such structures, a geometric processing step called triangulation is often employed to transform them into the STL format before sending them to 3D printers. Because lattice structures tend to have high geometric complexity, this step usually generates a large amount of triangles, a memory and compute-intensive task. This problem manifests itself clearly through large-scale lattice structures that have millions or billions of struts. To address this problem, this paper proposes to transform a lattice structure into an intermediate model called meta-mesh before undergoing real triangulation. Compared to triangular meshes, meta-meshes are very lightweight and much less compute-demanding. The meta-mesh can also work as a base mesh reusable for conveniently and efficiently triangulating lattice structures with arbitrary resolutions. A CPU+GPU asynchronous meta-meshing pipeline has been developed to efficiently generate meta-meshes from lattice structures. It shifts from the thread-centric GPU algorithm design paradigm commonly used in CAD to the recent warp-centric design paradigm to achieve high performance. This is achieved by a new data compression method, a GPU cache-aware data structure, and a workload-balanced scheduling method that can significantly reduce memory divergence and branch divergence. Experimenting with various billion-scale lattice structures, the proposed method is seen to be two orders of magnitude faster than previously achievable.

CerAMfacturing of a ceramic aerospike engine

High-performance ceramics are recognized as key enabling materials because of their very high thermal, chemical and mechanical resistance in combination with a lower density compared to most metals. These properties apparently predestine ceramics for many different applications, e.g. propulsion engines.Within the present work, the high potential of additive manufacturing in the field of ceramic materials (CerAMfacturing) could be demonstrated. It is possible to produce high-resolution ceramic aerospike engines using CerAM VPP. The manufactured components not only combine the typical ceramic properties, but also exhibit:- high geometric complexity,- very good surface properties, and- very good manufacturing accuracy.These developments will pave the way for a new generation of propulsion engines.

Influence of contour processing parameters on porosity, surface roughness, and fatigue life in laser powder bed Al-10Si-0.4Mg

Additive manufacturing (AM) enables fabrication of net-shape components with high geometric complexity, however mechanical properties of parts with as-fabricated surfaces are limited by surface roughness and subsurface porosity that may initiate fatigue cracks. This study aims to improve the utility of parts with as-fabricated surfaces by quantifying processing-surface condition-fatigue performance relationships, optimizing surface contour parameters for fatigue performance, and investigating the application of surface profilometry as a non-destructive quality control method. Eleven contour parameter sets of varying energy densities were produced in three classifications: no contour passes, single contour passes, and contours with secondary healing passes. Surface roughness was evaluated using line scan profilometry and subsurface porosity by optical microscopy. Contouring reduced the average surface roughness from Ra ≅ 25 μm to Ra ≅ 5 μm, and the layer of subsurface porosity introduced by this process could be partially eliminated by a secondary healing pass. One set of processing conditions from each contour class was selected for fatigue studies (R = -1), and their results were compared with the fatigue data from machined and polished specimens to evaluate the relative influence of surface condition. Relative to machined and polished specimens, fatigue lives are approximately five times lower for contoured specimens (with and without a healing pass) and twenty times lower for non-contoured specimens at equivalent stress levels up to 107 cycles to failure. Fractographic measurements of initial defect √area of all specimens reveal that fatigue behavior is well characterized by stress intensity factor amplitude across all surface conditions. An extreme value statistical method is developed using profilometry data to predict S-N curves, and it is concluded that non-destructive surface roughness measurements may be suitable for estimating the fatigue lives of components with as-fabricated surfaces as a rapid method for quality control.

Evolution of Self‐Assembled Lignin Nanostructure into Dendritic Fiber in Aqueous Biphasic Photocurable Resin for DLP‐Printing

AbstractThe design of lignin nanostructures where interfacial interactions enable enhanced entanglement of colloidal networks can broaden their applications in hydrogel‐based materials and light‐based 3D printing. Herein, an approach for fabricating surface‐active dendritic colloidal microparticles (DCMs) characterized by fibrous structures using nanostructured allylated lignin is proposed for the development of lignin‐based photocurable resins. With allyl‐terminated surface functionality of 0.61 mmol g−1, the entanglement between lignin‐DCM fibrils with a size of 1.4 µm successfully produces only lignin‐based hydrogels with structural integrity through photo‐crosslinking. The colloidal network of lignin dendricolloids reinforces the poly(ethylene glycol) (PEG) hydrogels during a digital light processing (DLP) 3D printing process by generating bicontinuous morphologies, resulting in six‐fold increases in toughness values with respect to the neat PEG hydrogel. The dual effectiveness of photoabsorption and free‐radical reactivity of lignin‐DCMs allow the light‐patterning of rather dilute PEG hydrogels (5–10%) with high geometric fidelity and structural complexity via DLP 3D printing. This study demonstrates a green and effective strategy for the design of 1D lignin‐DCMs that increases the versatility of the nanostructured biopolymer, opening up numerous opportunities for formulating functional hydrogels with robust structure‐property correlations.

A new evolutionary optimization algorithm with hybrid guidance mechanism for truck-multi drone delivery system

Synchronization of the Traveling Salesman Problem with Drone (TSP-D) is one of the most complex NP-hard combinatorial routing problems in the literature. The speeds, capacities and optimization constraints of the truck-drone pair are different from each other. These differences lead to the search space of TSP-D having a high geometric complexity and a large number of local solution traps. Being able to avoid local solution traps in the search space of TSP-D and accurately converge to the global optimal solution is the main challenge for evolutionary search algorithms. The way to overcome this challenge is to dynamically adapt exploitation and exploration behaviors during the search process and maintain these two in a balanced manner depending on the geometric structure of TSP-D's search space. To overcome this challenge, research consisting of three steps was conducted in this article: (i) three different guide selection methods, namely greedy, random and FDB-score based, were used to provide exploitation, exploration and balanced search capabilities, (ii) by hybridizing these three methods at different rates, guide selection strategies with different search capabilities were developed, (iii) by associating these hybrid guide selection strategies with different stages of the search process, the guidance mechanism was given a dynamic behavioral ability. Thus, the Fitness-Distance Balance-based evolutionary search algorithm (FDB-EA) was designed to achieve a sustainable exploitation-exploration balance in the search space of TSP-D and stably avoid local solution traps. To test the performance of the FDB-EA, the number of delivery points was set to 30, 50, 60, 80, and 100 and compared with twenty-seven powerful and current competing algorithms. According to the non-parametric Wilcoxon pairwise comparison results, FDB-EA outperformed all competing algorithms in all five different TSP-D problems. According to the results obtained from the stability analysis, the success rates and calculation times of FDB-EA, EA and AGDE algorithms were 88.00% (6308.79 sec), 58.40% (7377.43 sec) and 13.460% (34664.19 sec) respectively.

Effects of different cross-sections of Body Centered Cubic cells on pressure drop and heat transfer of additively manufactured heat sinks

In many industrial applications, heat loads management requires the design and production of compact heat exchangers which are expected to handle high thermal loads with acceptable pressure losses, while assuring good mechanical performances. These challenging targets can be achieved by filling the cavities where the cool/hot fluid circulates with lattice structures promoting the heat exchange between the fluid and the cavity boundaries. Such lattice structures can be only produced through Additive Manufacturing due to their high geometric complexity. Recent experimental investigations proved the effectiveness of some kinds of lattice structures having a circular cross section. Here the aerothermal behaviour of Body-Centred Cubic (BCC) lattice stagger arrays in a rectangular channel was experimentally investigated by considerably extending the previous studies to higher Reynolds numbers (up to 30′000) and to new types of lattice structures. Specifically, three new BCC structures having a cam-like, drop-like and elliptical cross section were explored in this work and compared against those having circular cross section. All the samples were manufactured by means of Laser Powder Bed Fusion and made from AlSi10Mg. At first, the heat exchangers were comprehensively characterized by means of optical non-destructive methods. Successively they were tested in a dedicated rig by imposing constant heat flux boundary conditions. The characteristics of the transitional or fully turbulent approaching flow to the test section are also reported thanks to dedicated flow field measurements performed by Particle Image Velocimetry. According to the obtained results, the BCC structure with the circular cross section of larger diameter is the most effective in terms of heat transfer, although it is largely penalized by the pressure losses. Similar heat transfer performances were achieved by the tapered cross section of elliptical shape with the advantage of a considerably lower friction factor. Pressure losses resulted almost identical for all the tapered cross sections but lower than those of the circular one having an equal frontal dimension. When considering the thermal performance factor the circular shape becomes unfavourable for Re>20′000, while the elliptical cross section is the best choice to efficiently promote heat transfer up to Re=30′000.

Avaliação da Influência dos Parâmetros de Processamento na Manufatura Aditiva do Aço VP50IM por PCGTAW

Abstract The wire arc additive manufacturing process or WAAM (Wire Additive Arc Welding) is recognized as a process able of making pieces of high geometric complexity, with mechanical properties comparable to those of the cast material. However, there are significant challenges associated with WAAM, such as undesirable microstructures and mechanical properties, high residual stresses and geometric distortion. This study aims to contribute to the selection of deposition parameters for VP50IM steel using WAAM via pulsed TIG (Tungsten Inert Gas) and characterization of the generated stacking, using the Central Composite Complete methodology, CCC. In this study, the peak (Cp) and base (Cb) current, wire feed speed during peak (Vap), base (Vab) and welding speed (Vs) were varied. The ideal parameter presented was Cp=200A, Cb=100A, Vap=2.9cm/min, Vab=1.2cm/min and Vs=20cm/min. Tensile tests showed up to 15% greater resistance in the samples in the longitudinal section in the welding direction compared to the transverse direction. Hardness tests demonstrated up to 9% less hardness at the center of the stack compared to the top and bottom. The fracture analysis of the specimens showed ductile fracture.

STUDY OF THE INFLUENCE OF TOOL GEOMETRY AND CUTTING MODES ON THE ACCURACY OF PROCESSING PARTS OBTAINED BY FDM PRINTING

FDM printing (rapid prototyping) is a production process during which you can quickly obtain parts with high geometric complexity, given dimensions and with low energy consumption. From the early stages, 3D printing was identified as a method of rapid prototyping of parts that have a high level of complexity and their manufacture by traditional methods (for example, mechanical processing on CNC machines) is impossible. Despite the advantages that additive manufacturing offers, it has not yet been incorporated into industrial mass production lines as an alternative to traditional technologies, due to the long manufacturing time of parts and the lack of repeatability. In some cases this may not be a serious problem, but in some industrial applications where high precision parts are required, there are limitations due to the surface roughness of the outer layer and the accuracy requirements. To eliminate these shortcomings, an additional processing process is required. The purpose of this study is to determine the most optimal parameters of the cutting tool and cutting modes (spindle rotation frequency, feed rate, depth of cut, diameter of the end mill, geometric parameters of the cutting edge) for mechanical processing of parts obtained by FDM printing from CoPET (PETg) plastic in order to obtain the specified parameters accuracy and surface roughness. The article describes the study of the work of four types of end mills, which differ in the material of the cutting part, geometry, number of cutting teeth, coating. A rectangular prism printed from CoPET plastic, without internal voids, 100% filling, was selected as a blank. Processing was carried out on a MIKRON WF-2 vertical milling machine. For end mills, the cutting process was investigated with two different strategies: milling of a closed groove over the entire working width of the mill; milling of an open groove with the side surface of the cutter. The suitability for effective processing of each type of tool is evaluated. Results were recorded and evaluated using an optical electron microscope.

Correlation of Energy Density and Manufacturing Variables of AA6061 through Laser Powder Bed Fusion and Its Effect on the Densification Mechanism

Aluminum alloy processing via additive manufacturing (AM) technologies has increased in usage during the last decade. AM now enables manufacturing complex geometries not previously achieved through traditional manufacturing. Aluminum is usually processed using laser powder bed fusion (LPBF) technologies, which are used to manufacture metallic components of high geometrical complexity and dimensional accuracy and good mechanical, electrical, and chemical properties, which is why this technology is quite popular at industrial levels. To further develop quality control systems and new aluminum alloys using LPBF, there is a need to establish a predictive relationship between the parameters of the material. A study was carried out to investigate the relationship between energy density and process parameters such as laser power, scan speed, hatch spacing, scan pattern, and laser focus and its influence on the densification mechanism in additively manufactured components with aluminum alloys. AA6061 was selected due to its wide usage in different industries, given its low density and high mechanical performance. Relative density was analyzed using the Archimedes principle, and the quality and morphology of the AA6061 powder were analyzed through metallographic analysis. The process parameter selection was performed according to the best results obtained according to the laser power and energy density factors. The best manufactured samples had an energy density between 30 and 40 J/mm3, with relative densities above 99%.

3D Printed Bioinspired Hierarchical Surface Structure with Tunable Wettability

Abstract Nature has examples of impressive surfaces and interfaces with diverse wettability stemming from superhydrophilicity to superhydrophobicity. The multiscale surface structures found in biological systems generally have high geometric complexity, which makes it challenging to replicate their characteristics, especially using traditional fabrication techniques. It is even more challenging to fabricate such complex microstructures with tunable wettability. In this paper, we propose a method to tune the wettability of a micro-scale surface by changing the geometrical parameters of embedded micro-structures in the surface. By taking inspiration from an insect (springtails), we designed micropillar arrays with different roughness by adjusting geometric parameters such as reentrant angle, pitch distance, and the number of spikes and pillars. This study shows that, by changing geometrical parameters in microscale, the apparent contact angle, and hence the surface wettability can be calibrated. The microscale pillars were fabricated using a precise micro direct light processing (uDLP) three-dimensional (3D) printer. Different printing parameters were studied to optimize the geometric parameters to fabricate 3D hierarchical structures with high accuracy and resolution. By controlling the size of different features of the pillar, pillar number, and layout of the mushroom-shaped micropillars, the wettability of the surface is possible to be tuned from a highly nonwetting liquid/material combination to highly wetting material. Such wettability tuning capability expands the design space for many biomedical and thermofluidic applications.

A case study of hybrid manufacturing of a Ti-6Al-4V titanium alloy hip prosthesis

Hybrid manufacturing (HM) is a process that combines additive manufacturing (AM) and subtractive manufacturing (SM). It is becoming increasingly recognized as a solution capable of producing components of high geometric complexity, while at the same time ensuring the quality of the surface finish, rigour and geometric tolerance on functional surfaces. This work aims to study the surface finish quality of an orthopaedic hip resurfacing prosthesis obtained by HM. For this purpose, test samples of titanium alloy Ti-6Al-4V using two Power Bed Fusion (PBF) processes were manufactured, which were finished by turning and 5-axis milling. It was verified that, upon the machining tests, no differences in Ra and Rt were found between the various types of AM. Regarding the type of SM used, 5-axis milling provided lower roughness results with a consistent value of Ra = 0.6 µm. The use of segmented circle mills in 5-axis milling proved to be an asset in achieving a good surface finish. This work successfully validated the concept of HM to produce a medical device, namely, an orthopaedic hip prosthesis.As far as surface quality is concerned, it could be concluded that the optimal solution for this case study is 5-axis milling.

High-solid-content paste for stereolithography self-supporting printing of Luneburg lenses with a high degree of geometric-design freedom

Luneburg lenses prepared by alumina stereolithography, have the advantages of low dielectric loss, corrosion resistance, and flexible structure. However, the supporting structure limits the degree of geometric design freedom. This paper analyzes the effects of modifiers and dispersants on paste properties from the molecular structure, which is different from the traditional process that doesn’t analyze the influence of similar mechanisms and binding site competition while ignoring the correlation. Based on the dispersant’s better flexibility bringing more obvious change in viscosity, we first determine the amount of dispersant and then adapt the amount of modifier to obtain self-supporting pastes of 82 wt % and 24.9 Pa.swithout gradation. The gain and beam deflection angle of Luneburg lenses with high geometric complexity and working bandwidth of 38° formed by stereolithography self-supporting process is matched to the simulation results without manufacturing defects, which provides a theoretical basis for more flexible design and manufacture of Luneburg lenses.

Continuous printing of bone-inspired hierarchical porous objects using video projection-based stereolithography

Bone-inspired porous structures have attracted intense attention in biomedical and mechanical applications due to their unique combination of strength, toughness, and low weight. Although layer-by-layer-based additive manufacturing techniques can build structures with high geometric complexity, it remains challenging to fabricate porous objects with gradient porosity and pore size ranging from nano- to micro-scale. This research investigates the video projection-based stereolithography (VP-SL) process, which is a continuous 3D printing process, also known as Continuous Liquid Interface Production (CLIP), for fabricating hierarchical porous structures with nano-, micro- and macro-scale pores and surface features. This work tested the hypothesis that tuning the printing speed during the VP-SL process can generate an imbalance between curing and resin recoating, resulting in pores in cured polymer. First, the VP-SL process, setup, and the mechanisms of forming micropores and nanowrinkles were introduced. Then, the printing range of the VP-SL process was characterized using curing depth experiments. To validate the efficiency and feasibility of the proposed approach, various three-dimensional hierarchical structures were fabricated with homogeneous and gradient porosity, and the effect of porosity distribution on mechanical properties was studied. Test cases were printed and characterized to validate the feasibility and efficiency of the proposed approach. The results demonstrated the capability of the VP-SL process and its potential in the production of gradient porous objects for various applications, such as scaffolds for custom bone implants.

Experimental Tribological Study on Additive Manufactured Inconel 718 Features Against the Hard Carbide Counter Bodies

Abstract In past years, machining processes have been required when fabricating the complex Inconel 718 parts, and these processes cause undesired tensile residual stresses. Inconel 718 also exhibits extreme work hardening throughout the machining process. To avoid these issues, recently, Inconel 718 parts with high geometric complexity and dimensional accuracy, the laser powder bed fusion (LPBF) process, which belongs to additive manufacturing, has been extensively used. These Inconel 718 parts with LPBF processing are frequently utilized in various industries, including aerospace, automotive, pharmaceutical, and food processing, because of their high strength, biocompatibility, and corrosion resistance. Wear resistance is essential in addition to these properties for designing and crushing applications. In this paper, tribological tests were conducted on the LPBF-processed Inconel 718 parts and compared to casted Inconel 718 parts against the four types of counter bodies, namely boron carbide, silicon carbide, tungsten carbide, and titanium carbide. The studies were carried out for 30 min with a constant load of 5 N, frequency of 10 Hz, and stroke length of 1 mm. In comparison to casted samples, LPBF-processed samples showed low coefficient of friction (COF) values. The highest COF was observed on the cast Inconel 718 against the tungsten carbide counter body. The wear mechanisms were studied using scanning electron microscopy.

Robust line segment mismatch removal using point-pair representation and Gaussian-uniform mixture formulation

Line segment matching (LSM) plays an important role in image matching, while it is always a challenging problem due to line fracture and high geometric complexity. In this paper, we propose a line segment mismatch removal to address the sensitivity to segment length and the fracture problem. The mismatch removal removes the massive false matches obtained by the descriptors like LBD. Specifically, we suggest calculating the endpoint error in LSM rather than the direction error in considering the error band and segment length. We propose a point-pair representation, and transform the LSM problem to be an intuitive point-pair alignment problem with a mixture model. The point-pair representation is more robust on the segment length than the linear equation representation used in RANSAC, which significantly preserves short line segments. Then, based on the point-pair representation, we propose a novel Directed Endpoint Drift method to solve the inherent fracture problem of line segments by allowing the endpoints to move along the line to complement the fracture. Compared with the recent learning-based method, the proposed method nearly doubles the number of correct matches on the remote sensing dataset. Especially, the recall is improved by 10% to 30% for both the short segments and the slightly fractured segments, compared with the popular RANSAC and MAGSAC++ methods. The code is available at https://github.com/shenliang16/DEpD.