Complex Shapes Research Articles

Unraveling nonlinear and spatial non-stationary effects of urban form on surface urban heat islands using explainable spatial machine learning

Under global warming, surface urban heat islands (SUHI) threaten human health and urban ecosystems. However, scant research focused on exploring the complex associations between urban form factors and SUHI at the county scale, compared with rich studies at the city scale. Therefore, this study simultaneously examined the nonlinear and spatial non-stationary association between SUHI and urban form factors (e.g., landscape structure, built environment, and industrial pattern) across 2321 Chinese counties. An explainable spatial machine learning method, combining the Geographically Weighted Regression, Random Forest, and Shapley Additive Explanation model, was employed to deal with nonlinearity, spatial non-stationary, and interpretability of modeling. The results indicate the remarkable spatial disparities in the relationship between urban form factors and SUHI. Landscape structure contributes the most in southern counties, while the built environment is more important in northeastern counties. The impact of building density and building height increases with the county size and becomes the main driver of urban heat in mega counties. Most urban form factors exhibit nonlinear impacts on SUHI. For example, urban contiguity significantly affects SUHI beyond a threshold of 0.93, while building density does so at 0.17. By comparison, the influence of shape complexity remains stable above a value of 7. Factors such as industrial density and diversity have a varied influence on SUHI between daytime and nighttime. The results of local explanations and nonlinear effects provide targeted regional mitigation strategies for urban heat.

Enhanced wear resistance of in-situ nanoscale TiC reinforced Ti composites fabricated by additive manufacturing

The poor wear resistance of Ti alloys limits its application in the orthopedic and dental surgery field. TiC as an ideal reinforcement phase can improve wear resistance due to its high hardness, low coefficient of friction, and good bio-compatibility. However, the wettability of the interface between the TiC and Ti matrix is poor, which leads to low mechanical properties and wear resistance. In situ TiC formed through the addition of a C source can enhance interfacial bonding. In this work, the Ti-graphite composites are prepared by Laser powder bed fusion (LPBF). On the one hand, the LPBF can prepare various complex porous shapes according to the personalized customized demand. On the other hand, the LPBF can provide a high cooling rate, suppressing grain growth and refining grains, and also can obtain equally distributed nanoscale TiC particles. The results show that the composites are mainly composed of Ti, TiC, and residual graphite. With the increase of graphite content, the amount of TiC increases and the grain becomes smaller, and the microhardness increases from 317 HV0.2 to 408 HV0.2. Meanwhile, the Ti-1.5 wt%Gr composite has a low friction coefficient, volume of wear, and wear rate, which is attributed to the high hardness of TiC particles and lubrication of graphite. The oxide on the surface of the wear mark is mainly composed of TiO2 and TiO. The main wear mechanisms of the composites are oxidation wear and adhesive wear.

Engineered Shape-Morphing Transitions in Hydrogels Through Suspension Bath Printing of Temperature-Responsive Granular Hydrogel Inks.

4D printing of hydrogels is an emerging technology used to fabricate shape-morphing soft materials that are responsive to external stimuli for use in soft robotics and biomedical applications. Soft materials are technically challenging to process with current 4D printing methods, which limits the design and actuation potential of printed structures. Here, a simple multi-material 4D printing technique is developed that combines dynamic temperature-responsive granular hydrogel inks based on hyaluronic acid, whose actuation is modulated via poly(N-isopropylacrylamide) crosslinker design, with granular suspension bath printing that provides structural support during and after the printing process. Granular hydrogels are easily extruded upon jamming due to their shear-thinning properties and their porous structure enables rapid actuation kinetics (i.e., seconds). Granular suspension baths support responsive ink deposition into complex patterns due to shear-yielding to fabricate multi-material objects that can be post-crosslinked to obtain anisotropic shape transformations. Dynamic actuation is explored by varying printing patterns and bath shapes, achieving complex shape transformations such as 'S'-shaped and hemisphere structures. Furthermore, stepwise actuation is programmed into multi-material structures by using microgels with varied transition temperatures. Overall, this approach offers a simple method to fabricate programmable soft actuators with rapid kinetics and precise control over shape morphing.

Quantification of feature shape complexity for the virtual prototypes and investigation of additive manufacturability

Quantification of feature shape complexity for the virtual prototypes and investigation of additive manufacturability

Evidence for magnetic boundary layer accretion in RU Lup

Context. It is well established that classical T Tauri stars accrete material from a circumstellar disk through magnetic fields. However, the physics regulating the processes in the inner (0.1 AU) disk is still not well understood. Aims. Our aim is to characterize the accretion process of the classical T Tauri Star RU Lup. Methods. Optical high-resolution spectroscopic observations with CHIRON and ESPRESSO were obtained simultaneously with photometric data from AAVSO and TESS. Results. We detected a periodic modulation in the narrow component of the He I 5876 line with a period that is compatible with the stellar rotation period, indicating the presence of a compact region on the stellar surface that we identified as the footprint of the accretion shock. We show that this region is responsible for the veiling spectrum, which is made up of a continuum component plus narrow line emission that fills in the photospheric lines. An analysis of the high-cadence TESS light curve reveals quasi-periodic oscillations on timescales shorter than the stellar rotation period, suggesting that the accretion disk in RU Lup extends inward of the corotation radius, with a truncation radius at ~2 R*. This is compatible with predictions from three-dimensional magnetohydrodynamic models of accretion through a magnetic boundary layer (MBL). In this scenario, the photometric variability of RU Lup is produced by a nonsta-tionary hot spot on the stellar surface that rotates with the Keplerian period at the truncation radius. We also qualitatively discuss how more complex hot spot shapes may generate the same variability pattern. The analysis of the broad components of selected emission lines reveals the existence of a non-axisymmetric, temperature-stratified flow around the star, in which the gas leaves the accretion disk at the truncation radius and accretes onto the star channeled by the magnetic field lines. The unusually rich metallic emission line spectrum of RU Lup might be characteristic of the MBL regime of accretion. Conclusions. Our extensive multiwavelength database of RU Lup reveals many similarities to predictions from the scenario of accretion through a magnetic boundary layer. Alternative explanations would require the existence of a hot spot with a complex shape, perhaps made of two brighter knots, or a warped structure in the inner disk.

Applying design complexity metrics for post-processing cost modeling in metal additive manufacturing

The recent Additive manufacturing (AM) literature has primarily concentrated on exploring new avenues for improving the current technology and its applicability. It has also delved into research investments aimed at addressing the last remaining prediction challenges associated with current AM processes, particularly focusing to surface quality, accuracy, and internal composition. These limitations can often be mitigated though the application of post-processing techniques. Such techniques are often very costly both in time and monetary terms.When it comes to the impact that shape complexity has on post-fabrication costs for AM parts, a gap in the literature is apparent. In recent years, more attention has been devoted to researching a general shape complexity metric. It has been suggested in the literature to combine multiple of complexity metrics techniques, to reach more comprehensive model. This aspect has not received enough attention in previous works. In addition, the relationship between shape complexity and post-processing costs has not been assessed. And there are no predictive models for post-processing costs based on complexity.In this study, AM shapes for prototyping application are analysed. In this regard, previously established complexity metrics are used, together with expert’s assessments of post-processing costs, to create a model capable of predicting post-processing costs. This is achieved through a regression analysis using costs and complexity metrics values. The result of this research are two regression models, named 7 V and Vol/Sur Models, capable of predicting post-processing costs for AM parts produced through DMLS techniques with SS316L stainless steel powder. The accuracy of the two models is discussed.

Build orientation optimization considering thermal distortion in additive manufacturing

Additive manufacturing (AM) technology enables the fabrication of three-dimensional objects with complex shapes and has been extensively applied in various industries. AM is a layer-wise fabrication process where a variety of factors affect manufacturing performance and product quality. One of the most important factor is the thermal distortion, which is caused by the high temperature gradients in the fabrication process. The thermal distortion is also influenced by the support structure of the printing object, and this distortion varies depending on the chosen build orientation. Additionally, for the overhang regions, extra supports are required for printing and will be removed in the post-processing. For the 3D printing process, the thermal distortion and support requirements are interconnected and linked to build orientation. To investigate suitable build orientation, the thermal distortion and support are quantified, and a multi-objective build orientation optimization method is proposed to obtain representative orientations. Based on the proposed method, 9 typical 3D shapes are evaluated. In addition, the single-objective build orientation optimization problem is studied and compared, and the influence of slicing layers per stage on the simulation accuracy and efficiency is discussed. The effectiveness and applicability of the method are verified, and representative directions can be obtained for different fabrication purposes.

Microhole Creation in FDM-Printed Sheet Polymers: A Punching Process Approach

Fused deposition modeling (FDM) 3D printing is one of the additive manufacturing processes that can make components with complex shapes, require no tools, are cheap, safe, and have minimal waste. Despite all the advantages of the FDM process, the inability of this technique to create holes on a micro scale can be a problem and limits its application. In this research, a combination of FDM and machining processes was carried out, where micro holes in FDM printed components were created using a punching process. The punching process is carried out by varying pressure and speed. Furthermore, the diameter of the hole and the quality of the sheared edge of the hole resulting from the punching process were evaluated through observation using an optical microscope. The results show that the holes resulting from the punching process have a better shape and diameter than the FDM process. Then, the analysis of the sheared edge from punching shows that pressure and speed significantly affect the surface quality of the resulting sheared edge, where the quality increases with increasing pressure and speed. In the end, the punching process was proven to create micro-scale holes in FDM-printed polymer, especially at minimum thickness.

A data-driven maximum entropy method for probability uncertainty analysis based on the B-spline theory

The probability density function (PDF) is quite important for structural reliability analysis; thus, accurate PDF modeling methods draw increasing attention. This paper proposes a novel metaheuristic data-driven paradigm of the maximum entropy method (MEM) based on the B-spline function theory. Firstly, a B-spline proxy of the MEM PDF is proposed for probability uncertainty analysis. We derive the parameter calculation formulation and calculate the undetermined parameters via the raw data of structural responses. Then, to determine the knots of the B-spline functions, we propose a novel data-driven approach with the aid of a powerful metaheuristic algorithm and the response data information. Different from the traditional MEM, the proposed method is a complete data-driven solution approach and does not involve the statistical moment calculation and the nonlinear equations composed of statistical moments. Combining the advantages of the B-spline theory and the MEM, the proposed method can reconstruct the response PDF with a complex shape, such as the PDF with multiple peaks or heavy tails. For verification, two numerical examples and one engineering example are analyzed, and compared with some classical PDF modeling methods. The results show that the proposed method is superior to the compared methods in terms of computational accuracy, when the same sample data is used.

Elaboration of p-type Ge doped MnSiy (1.73 < y < 1.77) thermoelectric legs with complex shapes by binder jetting additive manufacturing technique

Thermoelectric legs with complex geometries exhibit high potential interest for fatal heat conversion into electricity due to higher thermal dissipation. Additive manufacturing (AM)is a solution of choice to decrease the manufacturing cost of thermoelectric legs that are historically requiring lengthy and costly fabrication processes and it gives the possibility of custom legs geometry. Nevertheless, microcracks and high porosity are commonly present by using this kind of process for thermoelectric materials leading to by limited performances. This work reports the possibility to manufacture n type Ge doped MnSiy (1.73 < y < 1.77) thermoelectric legs with various shapes without microcracks and with low porosity using the binder jetting AM technique followed by a Spark Plasma Sintering (SPS) step. The Mn15Si26 phase in the samples is systematically present, the samples density can reach 98%. A power factor of 1130 µW.m-1K-2 was reached at 350°C which is within the range of those obtained by the conventional technique of elaboration, proving that the material is not degraded using this novel route. It is mainly due to the good density and the absence of microcracks. Elaboration of legs with various geometry (cuboid and layered) was done to demonstrate the interest of this innovation manufacturing route. The benefit impact of the geometry was evaluated in term of voltages by a comparison measurement between a cuboid and a layered sample for which a remarkable gain of 16.5% (at an applied temperature of 74.7°C) was obtained for the layered sample due to a better thermal dissipation.

NPTS-PK: A new point kernel code for fast calculation of 3D gamma radiation field

The point kernel integration method is commonly utilized for the analytical calculation of gamma radiation fields in the field of radiation protection and shielding design. This study introduces NPTS-PK, a program developed for rapid calculation of 3D gamma radiation fields based on the NPTS program and the point kernel integration method. Based on the geometric construction and input method of the NPTS program, NPTS-PK supports point kernel calculations for radiation sources and shielding structures of various complex shapes and materials. By calculating material attenuation coefficients using continuous energy cross-section parameters, combined with a more precise buildup factor calculation method, the accuracy of calculation results has been enhanced. Improvements in ray tracing processes and the implementation of the probability neighbor list method have accelerated geometric processing. To address the challenge of excessive computation time associated with large-scale grid counting in point kernel programs, NPTS-PK integrates several efficient acceleration techniques, elevating the speed of radiation field calculation by an order of magnitude. Tests on typical model and engineering scenario demonstrate that the deviation between NPTS-PK results and the reference values is within a few tens of percent, and the computational efficiency and accuracy are improved compared to the standard point kernel programs.

Numerical study of vehicle motion during water exit under combined lifting force and wave action

During the retrieval process of the deep-sea mining vehicle (DSMV), the stability of the retrieval system is strongly influenced by the interaction between the vehicle body and the surrounding seawater due to the vehicle's complex shape and wave motion. Naturally, the negative side effects of significant changes in the vehicle's attitude and the water exit position can only increase retrieval's challenge. To investigate the characteristic of the flow field of the DSMV, this study employs the computational fluid dynamics method based on the Reynolds-averaged Navier–Stokes equations integrating the volume-of-fluid multiphase flow model with a fifth-order Stokes-wave model to explore the attitude and displacement changes of the vehicle during the water exit process in the ocean wave environment. The results indicate that the wave phase and lifting force are the major effect factors in the DSMV's water exit process. An appropriate lifting force under a specific wave phase can effectively reduce attitude changes and positional drift of the DSMV during water exit, thereby enhancing recovery efficiency and stability.

Surface coal mining in drylands: A multiscale comparison of spatiotemporal patterns and environmental impacts between Inner Mongolia and Mongolia

Surface coal mining (SCM) poses a great threat to the environment. Previous studies have explored the speed and scale of SCM in the Mongolian Plateau, but landscape-based analysis is needed for creating actionable knowledge required for environmental policy-making. Thus, taking a landscape ecological approach, here we compared the spatiotemporal patterns and major environmental impacts of SCM between Inner Mongolia of China and Mongolia during 1975–2015 at multiple administrative levels. We found that the SCM area increased by nearly 40 times in Inner Mongolia and 11 times in Mongolia during the 40 years. The annual increase rate in terms of both area and number was greater in Inner Mongolia than in Mongolia during 1975–2010, but the order was reversed during 2010–2015. At the prefectural or aimag level, the SCM distribution exhibited considerable variations. In 2015, 44 % of the total SCM area was located in Baotou, Wuhai, and Ordos of Inner Mongolia and Ömnögovi of Mongolia in 2015. The spatiotemporal patterns of SCM were characterized by increases in patch size, shape complexity, clustering, and landscape fragmentation. We estimated that the surrounding ecosystems disturbed by mining were 14.72 times larger than the SCM sites themselves in Inner Mongolia and 21.10 times in Mongolia. More threatened species were potentially affected by SCM in Inner Mongolia than in Mongolia. The variations in the scope and speed of SCM between Inner Mongolia and Mongolia may be attributable to multiple factors, including the distribution of coal mines themselves, economic investments, and national and local policies. Our study provides scientific support for promoting China-Mongolia bilateral collaboration for curbing SCM expansion and mitigating its environmental impacts on the plateau.

Introduction to the Special Issues

Abstract Artificial intelligence (AI) is rapidly reshaping our world. As AI systems become increasingly autonomous and integrated into various sectors, fundamental ethical issues such as accountability, transparency, bias, and privacy are exacerbated or morph into new forms. This introduction provides an overview of the current ethical landscape of AI. It explores the pressing need to address biases in AI systems, protect individual privacy, ensure transparency and accountability, and manage the broader societal impacts of AI on labor markets, education, and social interactions. It also highlights the global nature of AI's challenges, such as its environmental impact and security risks, stressing the importance of international collaboration and culturally sensitive ethical guidelines. It then outlines three unprecedented challenges AI poses to copyright and intellectual property rights; individual autonomy through AI's “hypersuasion”; and our understanding of authenticity, originality, and creativity through the transformative impact of AI-generated content. The conclusion emphasizes the importance of ongoing critical vigilance, imaginative conceptual design, and collaborative efforts between diverse stakeholders to deal with the ethical complexities of AI and shape a sustainable and socially preferable future. It underscores the crucial role of philosophy in identifying and analyzing the most significant problems and designing convincing and feasible solutions, calling for a new, engaged, and constructive approach to philosophical inquiry in the digital age.

Multiple myeloma segmentation net (MMNet): an encoder-decoder-based deep multiscale feature fusion model for multiple myeloma segmentation in magnetic resonance imaging.

Patients with multiple myeloma (MM), a malignant disease involving bone marrow plasma cells, shows significant susceptibility to bone degradation, impairing normal hematopoietic function. The accurate and effective segmentation of MM lesion areas is crucial for the early detection and diagnosis of myeloma. However, the presence of complex shape variations, boundary ambiguities, and multiscale lesion areas, ranging from punctate lesions to extensive bone damage, presents a formidable challenge in achieving precise segmentation. This study thus aimed to develop a more accurate and robust segmentation method for MM lesions by extracting rich multiscale features. In this paper, we propose a novel, multiscale feature fusion encoding-decoding model architecture specifically designed for MM segmentation. In the encoding stage, our proposed multiscale feature extraction module, dilated dense connected net (DCNet), is employed to systematically extract multiscale features, thereby augmenting the model's sensing field. In the decoding stage, we propose the CBAM-atrous spatial pyramid pooling (CASPP) module to enhance the extraction of multiscale features, enabling the model to dynamically prioritize both channel and spatial information. Subsequently, these features are concatenated with the final output feature map to optimize segmentation outcomes. At the feature fusion bottleneck layer, we incorporate the dynamic feature fusion (DyCat) module into the skip connection to dynamically adjust feature extraction parameters and fusion processes. We assessed the efficacy of our approach using a proprietary dataset of MM, yielding notable advancements. Our dataset comprised 753 magnetic resonance imaging (MRI) two-dimensional (2D) slice images of the spinal regions from 45 patients with MM, along with their corresponding ground truth labels. These images were primarily obtained from three sequences: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and short tau inversion recovery (STIR). Using image augmentation techniques, we expanded the dataset to 3,000 images, which were employed for both model training and prediction. Among these, 2,400 images were allocated for training purposes, while 600 images were reserved for validation and testing. Our method showed increase in the intersection over union (IoU) and Dice coefficients by 7.9 and 6.7 percentage points, respectively, as compared to the baseline model. Furthermore, we performed comparisons with alternative image segmentation methodologies, which confirmed the sophistication and efficacy of our proposed model. Our proposed multiple myeloma segmentation net (MMNet), can effectively extract multiscale features from images and enhance the correlation between channel and spatial information. Furthermore, a systematic evaluation of the proposed network architecture was conducted on a self-constructed, limited dataset. This endeavor holds promise for offering valuable insights into the development of algorithms for future clinical applications.

Exploring the Impact of Heat Treatment on Residual Stress, Microstructure, and Mechanical Behavior of Laser Powder Bed Fusion Additively Manufactured Ti-6Al-2Sn-4Zr-2Mo Alloy

In the field of additive manufacturing (AM), a promising alloy that is gaining attention is the near-α Ti-6Al-2Sn-4Zr-2Mo (Ti6242) alloy. This alloy is known for its remarkable resistance to creep and corrosion at elevated temperatures. However, the production of Ti6242 components via Laser-based AM technologies faced with several challenges, such as the formation of residual stress in the as-built state that originates from the nature of these processes. Therefore, this study has a dual objective: first, to evaluate the microstructure and residual stresses in the as-built Ti6242 produced through laser powder bed fusion. Secondly, to investigate the impact of a promising post heat treatment on mitigating residual stresses, inducing alterations in the microhardness of the Ti6242 alloy and its ductility, phase transformations and microstructure evolution. The as-built specimen exhibited a significant residual stress of 1357 MPa, which was caused by the pronounced temperature gradients experienced during the fabrication process. It is interesting to note that this promising post heat treatment was highly effective in eliminating 99% of the residual stress. After heat treatment, the amount of α' present in the as-built specimen decreased. This resulted in the activation of diffusion of elements such as molybdenum, causing a proportion of the α' to decompose into the more ductile α+β structure. Additionally, it is revealed that the microstructural changes and relieved stresses from the heat treatment process caused a lower microhardness and a higher ductility under compressive loading, as the elongation and toughness reached 20.5% and 23.255 kJ/mm3 in the heat-treated samples, respectively. The outcomes of this work facilitate the use of this alloy in the production of complex shape components through laser-based AM technologies.

Simulating irregular symmetry breaking in gut cross sections using a novel energy-optimization approach in growth-elasticity

Growth-elasticity (also known as morphoelasticity) is a powerful model framework for understanding complex shape development in soft biological tissues. At each instant, by mapping how continuum building blocks have grown geometrically and how they respond elastically to the push-and-pull from their neighbors, the shape of the growing structure is determined from a state of mechanical equilibrium. As mechanical loads continue to be added to the system through growth, many interesting shapes, such as smooth wavy wrinkles, sharp creases, and deep folds, can form on the tissue surface from a relatively flatter geometry.Previous numerical simulations of growth-elasticity have reproduced many interesting shapes resembling those observed in reality, such as the foldings on mammalian brains and guts. In the case of mammalian guts, it has been shown that wavy wrinkles, deep folds, and sharp creases on the interior organ surface can be simulated even under a simple assumption of isotropic uniform growth in the interior layer of the organ. Interestingly, the simulated patterns are all regular along the tube’s circumference, with either all smooth or all sharp indentations, whereas some undulation patterns in reality exhibit irregular patterns and a mixture of sharp creases and smooth indentations along the circumference. Can we simulate irregular indentation patterns without further complicating the growth patterning?In this paper, we have discovered abundant shape solutions with irregular indentation patterns by developing a Rayleigh–Ritz finite-element method (FEM). In contrast to previous Galerkin FEMs, which solve the weak formulation of the mechanical-equilibrium equations, the new method formulates an optimization problem for the discretized energy functional, whose critical points are equivalent to solutions obtained by solving the mechanical-equilibrium equations. This new method is more robust than previous methods. Specifically, it does not require the initial guess to be near a solution to achieve convergence, and it allows control over the direction of numerical iterates across the energy landscape. This approach enables the capture of more solutions that cannot be easily reached by previous methods. In addition to the previously found regular smooth and non-smooth configurations, we have identified a new transitional irregular smooth shape, new shapes with a mixture of smooth and non-smooth surface indentations, and a variety of irregular patterns with different numbers of creases. Our numerical results demonstrate that growth-elasticity modeling can match more shape patterns observed in reality than previously thought.

Numerical investigation on machining of additively manufactured CFRP composite with different build orientations and layer widths

Additive manufacturing (AM) is now a widely researched manufacturing technology in the past two decades to adopt in industry for its added advantage of customization and adopting complex geometries with comparatively low buy-to-fly ratio. Carbon fiber reinforced polymer (CFRP) composite has found applications in different high-performance industries for its greatest benefit of high strength-to-weight ratio. Additive manufacturing of CFRP composite (AM-CFRP) opens up the possibility of enhancing mechanical strength using different printing orientations and enables producing complex shapes maintaining sustainable manufacturing perspective. However, Limitation of AM parts of having surface irregularities and questionable dimensional accuracies. To use AM-CFRP parts in high precision assembly or industrial applications, post processing machining is often required to meet the customers’ specification of geometrical tolerances and acceptable surface finish. In this study, the influence of AM parameters on machinability of AM-CFRP composite has been evaluated using finite element analysis (FEA) based numerical simulation. The slot milling operation was simulated with a tungsten carbide end milling tool and AM-CFRP workpiece with four different printing directions, i.e., 0°-90°, 45°-135°, 0°-90°-45°-135°, two different layer widths, i.e., 50 µm and 100 µm, and two in-fill patterns, i.e., solid and perforated structures. The machinability of the 3D printed CFRP has been analyzed based on cutting forces, stress at first contact and maximum stress generation, and temperature increases at the interface during slot milling of AM-CFRP under different AM parameters. Evolution of failure mechanisms of AM-CFRPs under various machining conditions, such as, delamination, matrix rupture etc., have been discussed and chip and burr formation mechanisms have been analyzed. Finite Element analysis (FEA) package ABAQUS/Explicit was used to model 3D micro slot milling operation of AM-CFRP workpiece with appropriate damage and constitutive models, such as, damage initiation, progression and cohesion in adjacent passes and layers.

Non-planar 5-axis directional additive manufacturing of plastics: Machine, process, and tool path

Additive manufacturing (AM) of plastics is a rising research and application area for light weighting. The conventional way of AM is conducted layer-by-layer, whereas recently non-planar AM is proposed with several advantages such as better use of the material through decreased weight-to-strength ratio, reuse of support material and increased conformity for AM of complex shapes. As for all the tool path-based manufacturing processes, generation of the tool path, selection of process parameters and the machine tool configuration significantly affects the product quality. In this study, non-planar AM of plastics is handled in a holistic manner covering tool path, process, and machine perspectives. Tool path generation for non-planar and directional AM is discussed in accordance with the CAD-CAM software, where the tool path is generated using a novel approach which works with the bead directions obtained from FEM based mechanical analysis. The effects of critical process parameters on the geometrical conformity for non-planar AM are discussed through visual inspection. The proposed cluster-based tool path planning is successfully shown demonstrated from the geometrical perspective.

Atomic Structure and 3D Shape of a Multibranched Plasmonic Nanostar from a Single Spatially Resolved Electron Diffraction Map.

Despite the interest in improving the sensitivity of optical sensors using plasmonic nanoparticles (NPs) (rods, wires, and stars), the full structural characterization of complex shape nanostructures is challenging. Here, we derive from a single scanning transmission electron microscope diffraction map (4D-STEM) a detailed determination of both the 3D shape and atomic arrangement of an individual 6-branched AuAg nanostar (NS) with high-aspect-ratio legs. The NS core displays an icosahedral structure, and legs are decahedral rods attached along the 5-fold axes at the core apexes. The NS legs show an anomalous anisotropic spatial distribution (all close to a plane) due to an interplay between the icosahedral symmetry and the unzipping of the surfactant layer on the core. The results significantly improve our understanding of the star growth mechanism. This low dose diffraction mapping is promising for the atomic structure study of individual multidomain, multibranched, or multiphase NPs, even when constituted of beam-sensitive materials.