Geometric Accuracy Research Articles

Vision based process monitoring in wire arc additive manufacturing (WAAM)

AbstractA stable welding process is crucial to obtain high quality parts in wire arc additive manufacturing. The complexity of the process makes it inherently unstable, which can cause various defects, resulting in poor geometric accuracy and material properties. This demands for in-process monitoring and control mechanisms to industrialize the technology. In this work, process monitoring algorithms based on welding camera image analysis are presented. A neural network for semantic segmentation of the welding wire is used to monitor the working distance as well as the horizontal position of the wire during welding and classic image processing techniques are applied to capture spatter formation. Using these algorithms, the process stability is evaluated in real time and the analysis results enable the direction independent closed-loop-control of the manufacturing process. This significantly improves geometric fidelity as well as mechanical properties of the fabricated part and allows the automated production of parts with complex deposition paths including weld bead crossings, curvatures and overhang structures.

Full-length radiograph based automatic musculoskeletal modeling using convolutional neural network

Full-length radiographs contain information from which many anatomical parameters of the pelvis, femur, and tibia may be derived, but only a few anatomical parameters are used for musculoskeletal modeling. This study aimed to develop a fully automatic algorithm to extract anatomical parameters from full-length radiograph to generate a musculoskeletal model that is more accurate than linear scaled one. A U-Net convolutional neural network was trained to segment the pelvis, femur, and tibia from the full-length radiograph. Eight anatomic parameters (six for length and width, two for angles) were automatically extracted from the bone segmentation masks and used to generate the musculoskeletal model. Sørensen-Dice coefficient was used to quantify the consistency of automatic bone segmentation masks with manually segmented labels. Maximum distance error, root mean square (RMS) distance error and Jaccard index (JI) were used to evaluate the geometric accuracy of the automatically generated pelvis, femur and tibia models versus CT bone models. Mean Sørensen-Dice coefficients for the pelvis, femur and tibia 2D segmentation masks were 0.9898, 0.9822 and 0.9786, respectively. The algorithm-driven bone models were closer to the 3D CT bone models than the scaled generic models in geometry, with significantly lower maximum distance error (28.3 % average decrease from 24.35 mm) and RMS distance error (28.9 % average decrease from 9.55 mm) and higher JI (17.2 % average increase from 0.46) (P < 0.001). The algorithm-driven musculoskeletal modeling (107.15 ± 10.24 s) was faster than the manual process (870.07 ± 44.79 s) for the same full-length radiograph. This algorithm provides a fully automatic way to generate a musculoskeletal model from full-length radiograph that achieves an approximately 30 % reduction in distance errors, which could enable personalized musculoskeletal simulation based on full-length radiograph for large scale OA populations.

Design of Sustainable Aluminium-Based Feedstocks for Composite Extrusion Modelling (CEM).

Additive manufacturing (AM) has become one of the most promising manufacturing techniques in recent years due to the geometric design freedom that this technology offers. The main objective of this study is to explore Composite Extrusion Modelling (CEM) with aluminium as an alternative processing route for aluminium alloys. This process allows for working with pellets that are deposited directly, layer by layer. The aim of the technique is to obtain aluminium alloy samples for industrial applications with high precision, without defects, and which are processed in an environmentally friendly manner. For this purpose, an initial and preliminary study using powder injection moulding (PIM), necessary for the production of samples, has been carried out. The first challenge was the design of a sustainable aluminium-based feedstock. The powder injection moulding technique was used as a first approach to optimise the properties of the feedstock through a combination of water-soluble polymer, polyethyleneglycol (PEG), and cellulose acetate butyrate (CAB) wich produces low CO2 emissions. To do this, a microstructural characterisation was carried out and the critical solid loading and rheological properties of the feedstocks were studied. Furthermore, the debinding conditions and sintering parameters were adjusted in order to obtain samples with the required density for the following processes and with high geometrical accuracy. In the same way, the printing parameters were optimised for proper material deposition.

Fast Digital Orthophoto Generation: A Comparative Study of Explicit and Implicit Methods

A digital orthophoto is an image with geometric accuracy and no distortion. It is acquired through a top view of the scene and finds widespread applications in map creation, planning, and related fields. This paper classifies the algorithms for digital orthophoto generation into two groups: explicit methods and implicit methods. Explicit methods rely on traditional geometric methods, obtaining geometric structure presented with explicit parameters with Multi-View Stereo (MVS) theories, as seen in our proposed Top view constrained Dense Matching (TDM). Implicit methods rely on neural rendering, obtaining implicit neural representation of scenes through the training of neural networks, as exemplified by Neural Radiance Fields (NeRFs). Both of them obtain digital orthophotos via rendering from a top-view perspective. In addition, this paper conducts an in-depth comparative study between explicit and implicit methods. The experiments demonstrate that both algorithms meet the measurement accuracy requirements and exhibit a similar level of quality in terms of generated results. Importantly, the explicit method shows a significant advantage in terms of efficiency, with a time consumption reduction of two orders of magnitude under our latest Compute Unified Device Architecture (CUDA) version TDM algorithm. Although explicit and implicit methods differ significantly in their representation forms, they share commonalities in the implementation across algorithmic stages. These findings highlight the potential advantages of explicit methods in orthophoto generation while also providing beneficial references and practical guidance for fast digital orthophoto generation using implicit methods.

Quantitative and qualitative evaluation of three MSCT for high resolution bone imaging

IntroductionStrategies for achieving high resolution varies between manufacturers. In CT, the helical mode with narrow collimation has long been considered as the gold standard for high-resolution imaging. More recently, incremental modes with small dexels and focal spot, have been developed but have not been compared with helical acquisitions under optimal conditions. The aim of this work is to compare the high-resolution acquisition strategies currently proposed by recent MSCT. MethodsThree CT systems were compared. A phantom was used to evaluate geometric accuracy, uniformity, scan slice geometry, and spatial resolution. Human dry bones were used to test different protocols on real bone architecture. A blind visual analysis was conducted by trained CT users for classifying the different acquisitions (p-values). ResultsAll systems give satisfactory results in terms of geometric accuracy and uniformity. The in-plane MTF at 5% were respectively 13.4, 15.9 and 18.1 lp/cm. Dry-bones evaluation confirms that acquisition#3 is considered as the best. ConclusionsThe incremental acquisition coupled with à small focal spot, and a high-sampling detector, overpasses the reference of low-pitch helical acquisitions for high-resolution imaging. Cortical bone, bony vessels, and tumoral matrix analysis are the very next challenges that will have to be managed to improve normal and pathologic bone imaging thanks to the availability UHR-CT systems.

Reconstruction of 3D Information of Buildings from Single-View Images Based on Shadow Information

Accurate building geometry information is crucial for urban planning in constrained spaces, fueling the growing demand for large-scale, high-precision 3D city modeling. Traditional methods like oblique photogrammetry and LiDAR prove time consuming and expensive for low-cost 3D reconstruction of expansive urban scenes. Addressing this challenge, our study proposes a novel approach to leveraging single-view remote sensing images. By integrating shadow information with deep learning networks, our method measures building height and employs a semantic segmentation technique for single-image high-rise building reconstruction. In addition, we have designed complex shadow measurement algorithms and building contour correction algorithms to improve the accuracy of building models in conjunction with our previous research. We evaluate the method’s precision, time efficiency, and applicability across various data sources, scenarios, and scales. The results demonstrate the rapid and accurate acquisition of 3D building data with maintained geometric accuracy (mean error below 5 m). This approach offers an economical and effective solution for large-scale urban modeling, bridging the gap in cost-efficient 3D reconstruction techniques.

Respiratory motion modelling for MR-guided lung cancer radiotherapy: model development and geometric accuracy evaluation.

Objective.Respiratory motion of lung tumours and adjacent structures is challenging for radiotherapy. Online MR-imaging cannot currently provide real-time volumetric information of the moving patient anatomy, therefore limiting precise dose delivery, delivered dose reconstruction, and downstream adaptation methods.Approach.We tailor a respiratory motion modelling framework towards an MR-Linac workflow to estimate the time-resolved 4D motion from real-time data. We develop a multi-slice acquisition scheme which acquires thick, overlapping 2D motion-slices in different locations and orientations, interleaved with 2D surrogate-slices from a fixed location. The framework fits a motion model directly to the input data without the need for sorting or binning to account for inter- and intra-cycle variation of the breathing motion. The framework alternates between model fitting and motion-compensated super-resolution image reconstruction to recover a high-quality motion-free image and a motion model. The fitted model can then estimate the 4D motion from 2D surrogate-slices. The framework is applied to four simulated anthropomorphic datasets and evaluated against known ground truth anatomy and motion. Clinical applicability is demonstrated by applying our framework to eight datasets acquired on an MR-Linac from four lung cancer patients.Main results.The framework accurately reconstructs high-quality motion-compensated 3D images with 2 mm3isotropic voxels. For the simulated case with the largest target motion, the motion model achieved a mean deformation field error of 1.13 mm. For the patient cases residual error registrations estimate the model error to be 1.07 mm (1.64 mm), 0.91 mm (1.32 mm), and 0.88 mm (1.33 mm) in superior-inferior, anterior-posterior, and left-right directions respectively for the building (application) data.Significance.The motion modelling framework estimates the patient motion with high accuracy and accurately reconstructs the anatomy. The image acquisition scheme can be flexibly integrated into an MR-Linac workflow whilst maintaining the capability of online motion-management strategies based on cine imaging such as target tracking and/or gating.

Enhancing efficiency in photo chemical machining: a multivariate decision-making approach

Non-Traditional Machining (NTM) outperforms traditional processes by offering superior geometric and dimensional accuracy, along with a better surface finish. Photo Chemical Machining (PCM) represents one such NTM process, using chemical etching for material removal. PCM finds substantial application in the creation of microchannels in pharmaceutical, chemical and energy industries. Several input parameters—such as etchant concentration, etching time and etchant temperature—profoundly influence the machining’s quality and efficiency. Therefore, the optimization of these parameters is crucial. This study presents a comparative analysis of five Multiple Criteria Decision Making (MCDM) techniques—Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multi-Objective Optimization on the basis of Ratio Analysis (MOORA), Additive Ratio Assessment (ARAS), Weighted aggregated sum product assessment method (WASPAS) and Multi-Attributive Border Approximation Area Comparison Method (MABAC)—for the optimization of the PCM process. Key performance metrics considered are Material Removal Rate (MRR), Surface Roughness (SR), Undercut (Uc) and etch factor (EF). The weights of these criteria were calculated using the Criterion-Induced Aggregation Technique (CRITIC) and was compared with other popular methods like MEREC, Entropy and equal weights. MRR and EF are seen as beneficial criteria, while SR and Uc are perceived as cost criteria. Optimum process parameters were identified as 850 g/L etchant concentration, 40 min etching time and 70°C etchant temperature. Two of the three employed MCDM techniques agreed on these optimal parameters, reinforcing the findings. Furthermore, a strong correlation was observed amongst the employed MCDM techniques, further validating the results.

Study of the features of permanent and usual reverse-engineering methods of details of complex shapes

The subject of the study is sustainable reverse engineering and conventional reverse engineering of aviation equipment (AE) samples. The object of the study is the geometric accuracy of the extracted portrait of a part of a complex shape in comparison with the constructed analytical standard (AS). The work is aimed at researching the methods of sustainable reverse engineering and conventional reverse engineering on the example of the digitization of the box of the AE steering machine, chosen as a part of a complex shape. For this, a portrait of the "*.stl" and AS format file was created and compared by performing a control operation with the determination of the time spent. A structural and technological analysis of the box of the AE steering machine was carried out, which showed that the box has through holes of various diameters (from 10 to 41.6 mm) and shapes (square, trapezoidal, round); thin walls between holes (up to 1.6 mm); right angles and their rounding radii (up to 1–4 mm); the thickness of the body walls is 2.4 mm, etc. 3D scanner – ARTEC SPACE SPIDER (Luxembourg) was selected and scanning was performed. According to the analysis of research methods of reverse engineering, it was established that the use of permanent and conventional reverse engineering allows, in the first case, to quickly manufacture a part by 3D printing or milling on CNC machines, and in the second case, to create its AS with the provision of the specified geometric accuracy. The difference in time between permanent and normal reverse engineering was 8 hours in favor of the former. Control of the ideal portrait according to the AS of the AE steering machine body showed the maximum deviations from –0.30 mm to +0.23 mm and the minimum deviations from –0.04 mm to +0.08 mm. The smallest indicators were observed on vertical and horizontal planes, and the largest - in cities with plane slopes, corners and small radii. This made it possible to establish that the existing capabilities of the Geomagic Design X software for correcting the received portrait of the "*.stl" format file currently do not guarantee the provision of geometric accuracy requirements (up to ±0.5 mm) for the manufacture of an experimental part of a complex shape – the AE steering machine body with 3D printing. The resulting ideal portrait can be used to manufacture a part by milling on CNC machines, taking into account deviations at the stage of process model formation, which can become the topic of further research.

Additive Manufacturing of Metal Load‐Bearing Implants 1: Geometric Accuracy and Mechanical Challenges

AbstractAdditive manufacturing (AM) of load‐bearing metal implants allows sustainable production of personalized implants with complex shapes and inner architectures. Implants must meet strict requirements to not harm patients, and production technique must certify their performance. Available materials, many production parameters and implant personalization on patient's needs represent limits of AM. Layer‐by‐layer material deposition and repeated thermal cycles typical of AM may also cause discontinuities between layers affecting implant mechanical features, and its match to the host body. In this paper the mechanical challenges that AM must overcome to replace traditional manufacturing techniques are discussed, in order to better understand whether AM has to be limited to implant personalization for exceptional cases.

3-D Reconstruction and Measurement of Blade Profiles With Laser-Scanning Sensor via Multiview Registration Based on Dynamic Encoding of Feature-Coordinate Information

Currently, optical-based method provides a feasible means for blade reconstruction and measurement. However, as the key step, multi-view registration is still heavily reliant on the measurement system's motion stability and geometric accuracy. Moreover, the complex and high-reflective freeform surface of blades may result in both insufficient overlaps and less prominent overlap-area features between adjacent views, making accurate multi-view data alignment difficult. Thus, we propose a method to realize accurate 3-D reconstruction and measurement of blade profiles based on a scanning system with a laser-sensor and a coarse-to-fine registration strategy. Firstly, coarse alignment of the multi-view data is achieved by calibrating the system's rotational axis using the blade datum plane feature, which can provide a good initial value for the fine registration and improve the reconstruction efficiency. Then, a fine registration algorithm based on the dynamic encoding of feature-coordinate fusion information is proposed to refine the coarsely aligned data and reduce the effect of system motion error on registration accuracy. Here, the introduction of fusion information can effectively eliminate the redundant correspondences to continuously optimize the matching probability between multi-view data in each iteration, improving registration accuracy and efficiency. Finally, experiments on typical blades and comparison with the other fine registration algorithms demonstrate the accuracy and robustness of the proposed method.

On the Influence of Wave-Shaped Tool Path Strategies on Geometric Accuracy in Incremental Sheet Forming

In incremental sheet forming (ISF), the geometrical accuracy is still a challenge that is only solved for specific applications. The underlying mechanisms of geometrical defects in ISF are very complex and still not fully understood. Nevertheless, the process understanding is constantly evolving. Recent work has shown, for example, how bending moments resulting from residual stresses affect geometric accuracy. It has become clear that resulting bending moments with an axis parallel to the main tool path direction are dominant. Based on that, the current paper investigates the hypothesis that linear and parallel tool paths lead to an unfavourable accumulation of residual bending moments along a common axis, and whether this accumulation effect can be reduced by wave-shaped tool paths. Thus, the described research investigates the influence of novel path strategies on the residual bending moments and the resulting geometrical deviations. The path strategies are based on wave-shaped path lines, whereas the curvature is within the sheet plane. The investigations focussed on a rectangular sheet that is clamped at its shortest edges and a part geometry-sensitive to springback. Experimental and numerical investigations show a significantly positive influence of some investigated path strategies on the geometric deviation, compared to a conventional path strategy.

Clinical benchmarking of a commercial software for skin dose estimation in cardiac, abdominal, and neurology interventional procedures.

Radiation exposure from interventional radiology (IR) could lead to potential risk of skin injury in patients. Several dose monitoring software like radiation dose monitor (RDM) were developed to estimate the patient skin dose (PSD) distribution in IR. This study benchmarked the accuracy of RDM software in estimating PSD as compared to GafChromic film baseline in-vivo measurements on patients during cardiac, abdominal, and neurology IR procedures. The prospective study conducted in four IR departments included 81 IR procedures (25 cardiac, 31 abdominal, and 25 neurology procedures) on three angiographic systems. PSD and field geometry were measured by placing GafChromic film under the patient's back. Statistical analyses were performed to compare the software estimation and film measurement results in terms of PSD and geometric accuracy. Median values of measured/calculated PSD were 1140/1005, 591/655.9, and 538/409.7mGy for neurology, cardiac, and abdominal procedures, respectively. For all angiographic systems, the median (InterQuartile Range, IQR) difference between calculated and measured PSD was -10.2% (-21.8%-5.7%) for neurology, -4.5% (-19.5%-15.5%) for cardiac, and -21.9% (-38.7%--3.6%) for abdominal IR procedures. These differences were not significant for all procedures (p>0.05). Discrepancies increased up to -82% in lower dose regions where the measurement uncertainties are higher. Regarding the geometric accuracy, RDM correctly reproduced the skin dose map and estimated PSD area dimensions closely matched those registered on films with a median (IQR) difference of 0cm (-1-0.8cm). RDM is proved to be a useful solution for the estimation of PSD and skin dose distribution during abdominal, cardiac and neurology IR procedures despite a geometry phantom which is not specific to the latter type of IR procedures.

Process qualification, additive manufacturing, and postprocessing of a hydrogen peroxide/kerosene 6 kN aerospike breadboard engine

This contribution addresses the complete process chain of an annular aerospike breadboard engine fabricated by laser powder bed fusion using the nickel-based superalloy Inconel® 718. In order to qualify the material and process for this high-temperature application, an extensive material characterization campaign including density and roughness measurements, as well as tensile tests at room temperature, 700, and 900 °C, was conducted. In addition, various geometric features such as triangles, ellipses, and circular shapes were generated to determine the maximum unsupported overhang angle and geometrical accuracy. The results were taken into account in the design maturation of the manifold and the cooling channels of the aerospike breadboard engine. Postprocessing included heat treatment to increase mechanical properties, milling, turning, and eroding of interfaces to fulfill the geometrical tolerances, thermal barrier coating of thermally stressed surfaces for better protection of thermal loads, and laser welding of spike and shroud for the final assembly as well as quality assurance. This contribution goes beyond small density cubes and tensile samples and offers details on the iterations necessary for the successful printing of large complex shaped functional parts. The scientific question is how to verify the additive manufacturing process through tensile testing, simulation, and design iterations for complex geometries and reduce the number of failed prints.

3D Reconstruction of Ancient Buildings Using UAV Images and Neural Radiation Field with Depth Supervision

The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures of ancient structures and geometric issues caused by repeated textures. Additionally, there is a hash conflict problem when rendering outdoor scenes using neural radiation fields. To address these challenges, this paper proposes a 3D reconstruction method based on depth-supervised neural radiation fields. To enhance the representation of the geometric neural network, the addition of a truncated signed distance function (TSDF) supplements the existing signed distance function (SDF). Furthermore, the neural network’s training is supervised using depth information, leading to improved geometric accuracy in the reconstruction model through depth data obtained from sparse point clouds. This study also introduces a progressive training strategy to mitigate hash conflicts, allowing the hash table to express important details more effectively while reducing feature overlap. The experimental results demonstrate that our method, under the same number of iterations, produces images with clearer structural details, resulting in an average 15% increase in the Peak Signal-to-Noise Ratio (PSNR) value and a 10% increase in the Structural Similarity Index Measure (SSIM) value. Moreover, our reconstruction model produces higher-quality surface models, enabling the fast and highly geometrically accurate 3D reconstruction of ancient buildings.

Investigation of Microhole Quality of Nickel-Based Single Crystal Superalloy Processed by Ultrafast Laser

The geometric accuracy and surface quality of thin-film cooling holes have a significant impact on the cooling efficiency and fatigue life of aeroengine turbine blades. In this paper, we conducted experimental research on the processing of nickel-based single-crystal high-temperature alloy DD6 flat plates using different femtosecond laser processes. Our focus was on analyzing the effects of various laser parameters on the geometric accuracy results of microholes and the quality of the surfaces and inner walls of these holes. The results demonstrate that femtosecond laser processing has great influence on the geometrical accuracy and surface quality results of film cooling holes. Notably, the average laser power, focus position, and feed volume exert a significant influence on the geometric accuracy results of microholes. For instance, a higher laser power can damage the microhole wall, thereby leading to the formation of tiny holes and cracks. Additionally, microholes exhibit optimal roundness and taper values when using a zero defocus volume. Moreover, increasing the feed distance results in enhanced entrance and exit roundness, whereas scanning speed has a negligible impact on microhole roundness.

A continuous phase-evolution model for cold and strain-induced crystallization in semi-crystalline polymers

The dynamic crystallization during extreme thermomechanical history under producing or service strongly influences the dimensional stability of the semi-crystalline thermoplastic polymers. To produce high-precision structural components, the proper thermomechanical history needs to be carefully designed to eliminate the crystallization-induced shrinkage. On the contrary, the rapidly developed four-dimensional (4D) printing technology tries to amplify the crystallization-induced shrinkage to directly produce the complex curvatures without supporting materials. However, the crystals formed at different deformation states might have different orientations, leading to an anisotropic residual deformation. Therefore, tracing of all crystals formed with different initial configurations is the key issue to ensure the geometric accuracy of both the high-precision and the deformable components. In this study, we developed a continuous phase-evolution model for both the cold and the strain-induced crystallization in semi-crystalline thermoplastics. The irreversible cold crystallization is analogized as the raindrop falling into the pool, and the reversible strain-induced crystallization is analogized as the nonequilibrium liquid-gas phase transformation. Using the continuous phase-evolution concept, the crystal growth is described by a series of continuously formed crystal phases sequentially added into the initial amorphous medium. Each newly formed crystal phase is in a stress-free state at the formation moment, and therefore the crystallization history coupled with the whole deformation history can be memorized. By introducing the oriented growth tensor representing the stress-free state of all formed crystals, the deformation-history dependent anisotropic crystallization and the corresponding residual deformation can be traced. The developed model is validated by comparing with the experimental data of the dynamic crystallization in amorphous poly l-lactide polymers under thermomechanical loading cycles. Finally, the predictive capability of the model is illustrated by several demonstrations, to show the influences of deformation-history dependent crystallization orientations and the corresponding anisotropic residual deformation.

Numerical evaluation of cutting strategies for thin-walled parts

Static form errors due to in-process deflections is a major concern in flank milling of thin-walled parts. To increase both productivity and part geometric accuracy, there is a need to predict and control these form errors. In this work, a modelling framework for prediction of the cutting force-induced form errors, or thickness errors, during flank milling of a thin-walled workpiece is proposed. The modelled workpiece geometry is continuously updated to account for material removal and the reduced stiffness matrix is calculated for nodes in the engagement zone. The proposed modelling framework is able to predict the resulting thickness errors for a thin-walled plate which is cut on both sides. Several cutting strategies and cut patterns using constant z-level finishing are studied. The modelling framework is used to investigate the effect of different cut patterns, machining allowance, cutting tools and cutting parameters on the resulting thickness errors. The framework is experimentally validated for various cutting sequences and cutting parameters. The predicted thickness errors closely correspond to the experimental results. It is shown from numerical evaluations that the selection of an appropriate cut pattern is crucial in order to reduce the thickness error. Furthermore, it is shown that an increased machining allowance gives a decreased thickness error for thin-walled plates.

Effects of respiratory motion on accurate four-dimensional computed tomography acquisition and plan delivery for lung stereotactic body radiation therapy

AbstractThis study evaluated the impact of respiratory movement on four-dimensional computed tomography (4D-CT) acquisition and radiotherapy delivery. QUASAR™ Respiratory Motion Phantom with nine sinusoidal waveforms that combined three amplitudes (10, 20, and 30 mm) and three breaths per minute (BPMs) (10, 15, and 20) was used. We acquired 4D-CT for nine respective waveforms and measured and compared geometrical factors (volume, shape, and motion) to reference static CT. We optimized and delivered radiotherapy plans on a Vital Beam and evaluated gamma for dose distribution analysis with three acceptance criteria: 3%/3 mm, 3%/3 mm, and 5%/5 mm. Geometrical analysis revealed that an increase in amplitude decreased 4D-CT acquisition accuracy. Geometrical factors for an amplitude of ≤ 20 mm were within tolerance limits, however, amplitude 30 mm resulted in significant deviations over tolerance limits. BPM had a minimal effect on geometrical accuracy when accurate pitch values for respective BPMs were selected for scanning. Dose distribution analysis also indicated that an increase in amplitude decreased the gamma passing rates. Amplitude 30 mm failed to achieve a 95% passing rate for all three gamma evaluation criteria adopted in the present study. Overall, our results demonstrate that respiratory motion has significant effects on the accuracy of both 4D-CT acquisition and planned radiotherapy delivery. Small amplitude of ≤ 20 mm is required to ensure accuracy.

Bioinspired airwings: Design and additive manufacturing of a geometrically graded microscale maple seed

Additive manufacturing is a key enabler for the fabrication and industrial application of open-celled cellular lattice structures bearing significant potential for lightweight design with advantageous stiffness–weight ratio and reduced build time. For components with strut thicknesses in the micrometer region, however, the influence of the process dominates the quality and properties of the lattice component. This includes geometrical accuracy, surface roughness, internal porosity, nodal constrictions, and elongated grains in the microstructure. The utilization of process-property correlation in the additive design process is rarely shown in the available literature.In this contribution, we develop an ultralight thin-walled maple seed that exhibits a geometrically graded strut design to maintain center of gravity and load per area. This is done by a preliminary closed-form analytical consideration to determine the design space. Second, topology optimization is carried out to obtain a re-designable structure. Using non-dimensional scaling laws enables an accurate prediction of the desired strut thickness. The maple seed has been manufactured with each strut being assigned different process parameters according to the scaling law, coated with clear varnish, and subjected to a flight test as a proof of concept.