Additive manufacturing最新文献

{"title":"Role of gravity magnitude on flowability and powder spreading in the powder bed fusion additive manufacturing process: Towards additive manufacturing in space","authors":"Seungkyun Yim ,&nbsp;Hao Wang ,&nbsp;Kenta Aoyagi ,&nbsp;Kenta Yamanaka ,&nbsp;Akihiko Chiba","doi":"10.1016/j.addma.2024.104441","DOIUrl":"10.1016/j.addma.2024.104441","url":null,"abstract":"<div><div>Understanding powder spreading under low gravity conditions is essential for optimizing final products using additive manufacturing in space. In this study, we investigated the role of gravity on flowability and spreading mechanisms through combined experimental and discrete element method (DEM) studies. Three powders with different theoretical densities were used to reenact low compressive conditions resembling those in a low-gravity environment. The influence of low compressive conditions on flowability and spreading behavior was examined using the Hall flowmeter, rotating drum, and spreading experiments. In the experimental result, the static flowability was primarily affected by the presence of elongated particles rather than the compressive conditions. The dynamic AoR of TD_4 powder increased compared to that of TD_8 powder, despite the presence of spherical particles with a smooth surface finish. A DEM simulation study was conducted using TD_8 powder to investigate the impact of different gravity levels on dynamic flowability. The DEM studies revealed that the dynamic flowability under rotation was decreased under low gravity owing to the promoted cohesive interactions. The powder spreading experiment was performed using the three powders with different theoretical densities. The <em>in-situ</em> observation with particle image velocimetry analysis revealed that kinetic energy dissipation in the spreading process was accelerated in the TD_8 powder pile, despite its high interparticle friction and cohesive force. The powder spreading simulation was conducted using TD_8 powder to clarify the effect of low gravity on the powder spreading process. In TD_8 powder under <em>1 G</em>, particle supply was facilitated by a synergistic effect of free-falling and deposited particles. However, increased cohesive interactions under <em>0.5 G</em> and <em>0.16 G</em> restricted particle supply via free-falling, consequently reducing the powder bed density by about 2 % and 6.2 %, respectively. These findings prove that the cohesive force predominantly controls dynamic flowability and powder bed quality in the spreading process under low gravity conditions.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104441"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of spatter-related defects in metal laser powder bed fusion by analytical and machine learning modelling applied to off-axis long-exposure monitoring","authors":"Nicolò Bonato,&nbsp;Filippo Zanini,&nbsp;Simone Carmignato","doi":"10.1016/j.addma.2024.104504","DOIUrl":"10.1016/j.addma.2024.104504","url":null,"abstract":"<div><div>Laser powder bed fusion of metals is increasingly used for fabricating complex parts requiring good mechanical properties. Simultaneously, researchers in the field are intensifying the efforts to reduce defects, such as internal porosities, which hinder a wider industrial adoption of this technology, urging process monitoring to a pivotal role in defect identification and mitigation. Therefore, understanding the correlation between in-process monitoring signals and post-process actual defects is fundamental to taking informed decisions and potential corrective actions during the process. This work focuses on developing models to predict spatter-related defects from specific process signatures detected through off-axis long-exposure imaging. Layer-wise images were properly aligned with corresponding cross-sections from tomographic reconstructions to investigate the relationship between spatter-related signatures and actual defects measured by X-ray computed tomography. This relationship was used as a knowledge basis to develop an analytical image-processing approach and a machine learning-based methodology, which were then compared in terms of their correlation performances. The advantages and limitations of both methods are discussed in the paper. Both approaches led to promising results in the prediction of lack-of-fusion defects caused by spatters, with the machine learning approach showing a prediction accuracy in the order of 90 % for defects with equivalent diameter above 90 µm, while the analytical model needed equivalent diameters larger than 130 µm to reach a prediction accuracy in the order of 80 %. Furthermore, the machine learning method led to strong results regarding early defect detection, with most of the investigated defects properly predicted by analysing two consecutive layers after the signature detection.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104504"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Denoising diffusion probabilistic models for generative alloy design","authors":"Patxi Fernandez-Zelaia ,&nbsp;Saket Thapliyal ,&nbsp;Rangasayee Kannan ,&nbsp;Peeyush Nandwana ,&nbsp;Yukinori Yamamoto ,&nbsp;Andrzej Nycz ,&nbsp;Vincent Paquit ,&nbsp;Michael M. Kirka","doi":"10.1016/j.addma.2024.104478","DOIUrl":"10.1016/j.addma.2024.104478","url":null,"abstract":"<div><div>Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104478"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of nozzle geometry and fiber orientation on the tensile strength of 3D printed continuous fiber reinforced composites","authors":"Taasnim Ahmed Himika ,&nbsp;Louise Olsen-Kettle ,&nbsp;Dong Ruan ,&nbsp;Ali Daliri","doi":"10.1016/j.addma.2024.104490","DOIUrl":"10.1016/j.addma.2024.104490","url":null,"abstract":"&lt;div&gt;&lt;div&gt;In material extrusion (MEX) based 3D printing, inter-filament voids are intrinsic to printing process. The void orientation, volume and shape are affected by multiple factors including the nozzle shape, stacking sequence and printing direction. In this study, the adverse effects of the inter-filament voids on tensile properties and damage modes were investigated numerically on 3D printed acrylonitrile butadiene styrene-carbon fiber (ABS/CF) continuous fiber-reinforced composites (CFRCs). Uniaxial tensile simulations were performed considering various nozzle geometries (circular, square), fiber orientations (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;θ&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mn&gt;9&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) relative to loading direction, and a regular stacking sequence of extrudates. The extrudate cross-section was modeled either using elliptical or superelliptical extrudates deposited from a circular or square nozzle, respectively. Excellent agreement was seen when the simulated results were benchmarked against several published experimental and numerical work. Simulated results showed that changing the nozzle shape from circular to square improved the mechanical properties across all fiber angles by lowering the void content by &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;7&lt;/mn&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;8&lt;/mn&gt;&lt;mtext&gt;%&lt;/mtext&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and increasing the ultimate tensile strength (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;σ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) by &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;11&lt;/mn&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;18&lt;/mn&gt;&lt;mtext&gt;%&lt;/mtext&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, tensile stiffness (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) by &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;8&lt;/mn&gt;&lt;mtext&gt;%&lt;/mtext&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, and the tensile failure strains (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;ϵ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) by &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;11&lt;/mn&gt;&lt;mtext&gt;%&lt;/mtext&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. For superelliptical extrudates the number of observed damage modes also reduced, and this is due to a 37.2% and 58.2% improvement in the inter-filament and inter-layer bond lengths, respectively. Also, when fiber angle became increasingly off-axis to tensile load direction, the strengths, moduli, and failure strains reduced for both circular and square nozzles. The significance of using microstructure geometries and explicitly modeling inter-filament voids for simulating MEX printed CFRCs was highlighted by comparing these results with both analytical calculations","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104490"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based rapid prediction of temperature field and intelligent control of molten pool during directed energy deposition process","authors":"Xiankun Cao ,&nbsp;Chenghong Duan ,&nbsp;Xiangpeng Luo ,&nbsp;Shaopeng Zheng ,&nbsp;Hangcheng Xu ,&nbsp;Xiaojie Hao ,&nbsp;Zhihui Zhang","doi":"10.1016/j.addma.2024.104501","DOIUrl":"10.1016/j.addma.2024.104501","url":null,"abstract":"<div><div>In this work, deep learning-based approaches are proposed to provide promising solutions to address the challenges in realizing intelligent manufacturing and digital twins for directed energy deposition (DED) process. Firstly, a rapid and accurate prediction of part temperature is realized by innovatively combining graph neural networks (GNNs) and recurrent neural networks (RNNs). Twenty parts with different structures are selected for demonstration. GPU parallel computing technique is adopted to accelerate the thermal finite element analysis, which is used to quickly construct the simulated graph dataset with sufficient samples. By embedding the memory optimization method into the GNN block, deeper GNNs with more trainable parameters are successfully trained with a 79.4 % lower GPU memory footprint, which solves the difficulty of deeper GNNs are hard to train on large graph datasets, and the accuracy of temperature prediction on unseen DED parts is significantly improved. Secondly, for intelligent molten pool regulation, a semi-analytic temperature solution method is used to create an efficient DED environment in reinforcement learning (RL) workflows. The intelligent control of molten pool depth under complex deposition strategy is realized based on the environmental state represented by molten pool images. A tailored convolutional neural networks (CNNs) model is employed as the agent to output varying laser power and continuously interact with the dynamic environment. Compared with the traditional artificial neural network agent, the total reward scored by the CNN agent is improved by 9.7 % in the zigzag deposition process, mitigating the fluctuations in the controlled molten pool depths. Moreover, CNNs are more compatible with in-situ thermal images. This work can provide theoretical and technical support for realizing real-time and even ahead-of-time temperature prediction and the corresponding feedback control during DED process.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104501"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of printing parameters in additive manufactured samples using fractal geometry of computed tomography images","authors":"Miguel-Angel Pardo-Vicente ,&nbsp;Pablo Pavón-Domínguez ,&nbsp;Daniel Moreno-Nieto ,&nbsp;Miriam Herrera-Collado","doi":"10.1016/j.addma.2024.104476","DOIUrl":"10.1016/j.addma.2024.104476","url":null,"abstract":"<div><div>Additive Manufacturing (AM) has already attained a reliable level of maturity, specifically Fused Filament Fabrication (FFF), emerging as the most widespread process. Concurrently, the industrial demand for these parts has increased, requiring the analysis of their internal geometry to determine the level of similarity achieved concerning the expected structures. This work aims to provide tools to characterize FFF parts by relating printing properties to geometrical variables. For this purpose, three samples were printed in Polylactic Acid (PLA) with three different layer heights and analyzed by X-ray Computed Tomography (CT). After processing the images, fractal analysis was carried out using the box-counting method on the voids that appear between the filaments in order to obtain the fractal dimension. The porosity of the voids was also calculated. The analysis identifies the parameters characterizing the voids as number, size, shape, and location. In contrast to traditional porosity studies, the novelty of this work is that fractal analysis provides information about shape and distribution of voids in a single value (fractal dimension). It was corroborated that the fractal dimension depends not only on porosity but also on the shape and location of the voids. Additionally, it was found that not all void parameters influence equally the geometrical variables; variables related to porosity (number and size of voids) are more relevant than shape and location. Finally, it was demonstrated that by knowing the parameters of layer height and extrusion flow, the ideal porosity and fractal dimension can be determined, and any deviation from these parameters indicates the geometric printing error incurred.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104476"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interlayer reinforced 3D printed concrete with recycled coarse aggregate: Shear properties and enhancement methods","authors":"Chao Liu ,&nbsp;Yukun Zhang ,&nbsp;Huawei Liu ,&nbsp;Yiwen Wu ,&nbsp;Shiwei Yu ,&nbsp;Chuihui He ,&nbsp;Zhan Liang","doi":"10.1016/j.addma.2024.104507","DOIUrl":"10.1016/j.addma.2024.104507","url":null,"abstract":"<div><div>In reinforced 3D printed concrete structures, the shear interaction between the rebar and the 3D printed filaments influences both the performance and safety of the concrete structures during service. In this study, the shear performance of the interlayer reinforced interface (IRI) of 3D printed concrete with recycled coarse aggregates (3DPRAC) was investigated, focusing on the pore structure characteristics of the interlayer interface. An enhancement method for the IRI was proposed by varying the number of anchored rebar nails (ARNs) and comparing the results with those of 3D printed concrete with natural coarse aggregates (3DPNAC) and 3D printed mortar (3DPM). The results showed that the ARNs significantly improved the IRI shear strength of 3DPRAC. Specifically, the IRI shear strength of 3DPRAC was 6.1 % lower than that of 3DPNAC but 43.6 % higher than that of 3DPM. By examining the structural characteristics of the rebar-3DPRAC bonding area, a relationship between pore defects and shear strength was established. This established relationship led to the proposal of a partition model for the interlayer bonding interface. A finite element model of 3D printed concrete incorporating real pore structure characteristics was developed to analyze the stress distribution and damage characteristics of the interface under shear stress. The fracture mode induced by local pores with the crack propagation mechanism was also analyzed. Finally, a unified formula for calculating the shear strength of the 3DPRAC IRI was derived based on the principles of virtual work and plastic limit theory. This study provides theoretical support for engineering applications of 3D printed reinforced concrete structures.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104507"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method","authors":"Juan-Sebastian Rincon-Tabares ,&nbsp;Mauricio Aristizabal ,&nbsp;Matthew Balcer ,&nbsp;Arturo Montoya ,&nbsp;Harry Millwater ,&nbsp;David Restrepo","doi":"10.1016/j.addma.2024.104488","DOIUrl":"10.1016/j.addma.2024.104488","url":null,"abstract":"<div><div>Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. To address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104488"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From filler to structure: Designing 3D-printable silicone elastomers with broadband electromagnetic interference shielding","authors":"Junrui Tan ,&nbsp;Guizhi Zhu ,&nbsp;Longfei Tan ,&nbsp;Qiong Wu ,&nbsp;Zhixu Liu ,&nbsp;Mingwei Yang ,&nbsp;Xianwei Meng","doi":"10.1016/j.addma.2024.104469","DOIUrl":"10.1016/j.addma.2024.104469","url":null,"abstract":"<div><div>Material extrusion has revolutionized the fabrication of silicone elastomers with intricate and customized structures. However, the trade-off between the ink printability and functional filler compounding impedes the advancement of 3D-printed silicone elastomers for applications such as electromagnetic interference (EMI) shielding and thermal management. In this study, we present a novel approach to fabricating functional silicone elastomers, focusing on the design of fillers, inks and structures. Ink printability was achieved by modified nanosheets, which conferred the thixotropy and self-support capacity to inks by constructing dynamic interfacial interactions within the silicone matrix. Additionally, modified nanosheets exhibited a “lubricating” effect under high shear rates owing to their layered structure, thereby facilitating a smooth extrusion process. Utilizing EMI shielding simulations of periodic porous structures as a guide, we successfully printed broadband EMI shielding silicone elastomers. Furthermore, the versatility of our approach was demonstrated through the creation of customized 3D-printed shielding boxes and wearable thermal management films, showcasing the diverse potential applications of the 3D-printed silicone elastomers. We anticipate that our innovative design approach will bridge the gap between functional elastomers and 3D printing technology, opening up new avenues for their applications in various fields.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104469"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stress-adaptive femur bionic triple periodic minimal heterostructures manufactured by SLS technology with excellent mechanical properties","authors":"Ruiyao Liu ,&nbsp;Guofeng Yao ,&nbsp;Qingyang Wang ,&nbsp;Nuo Yang ,&nbsp;Jundong Zhang ,&nbsp;Chaolei Zhang ,&nbsp;Yuancheng Zhu ,&nbsp;Xiang Li ,&nbsp;Zhenglei Yu ,&nbsp;Yunting Guo ,&nbsp;Zezhou Xu ,&nbsp;Peng Li ,&nbsp;Chunling Mao","doi":"10.1016/j.addma.2024.104457","DOIUrl":"10.1016/j.addma.2024.104457","url":null,"abstract":"<div><div>Inspired by the morphology and material distribution characteristics of femoral trabecular bone, four types of biomimetic triply periodic minimal surface (TPMS) heterogeneous structures were designed. Biomimetic samples were fabricated using selective laser sintering technology for quasi-static compression and impact testing. A comparative study of the planar compression performance and impact resistance of the biomimetic TPMS heterogeneous structures was conducted. The results showed that the heterogeneous component composition improved the strength performance of the original structure by over 25 %, and enhanced the overall energy absorption characteristics by more than 23.5 %. By leveraging the mechanical coupling properties of heterogeneous materials, the strength and energy absorption performance of the original structure were increased by over 20 %. Additionally, combining additive manufacturing technology, a novel stress-adaptive porous component design for practical engineering applications was developed. In conjunction with bicycle helmet design, the stress-adaptive component modeling method demonstrated excellent performance in modeling flexibility and mechanical strength. By reasonably combining different types of materials, the heterogeneity of materials can fully utilize their respective advantages and compensate for deficiencies, thereby creating materials with superior mechanical properties.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104457"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}