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 in-situ 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 1 G, particle supply was facilitated by a synergistic effect of free-falling and deposited particles. However, increased cohesive interactions under 0.5 G and 0.16 G 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.
了解低重力条件下的粉末铺展对于优化空间快速成型制造的最终产品至关重要。在本研究中，我们通过实验和离散元素法（DEM）相结合的研究，探讨了重力对流动性和铺展机制的作用。我们用三种理论密度不同的粉末重新演算了与低重力环境类似的低压缩条件。利用霍尔流量计、旋转滚筒和铺展实验考察了低压缩条件对流动性和铺展行为的影响。实验结果表明，静态流动性主要受细长颗粒的影响，而非压缩条件。尽管存在表面光滑的球形颗粒，但 TD_4 粉末的动态 AoR 比 TD_8 粉末有所增加。使用 TD_8 粉末进行了 DEM 模拟研究，以调查不同重力水平对动态流动性的影响。DEM 研究表明，在低重力条件下，由于内聚相互作用的促进，旋转下的动态流动性降低。使用三种理论密度不同的粉末进行了粉末铺展实验。利用颗粒图像测速仪分析进行的原位观测表明，尽管 TD_8 粉末堆具有较高的颗粒间摩擦力和内聚力，但在铺展过程中动能耗散加快。为了明确低重力对粉末铺展过程的影响，我们使用 TD_8 粉末进行了粉末铺展模拟。在重力为 1 G 的 TD_8 粉末中，自由落体和沉积颗粒的协同作用促进了颗粒的供应。然而，在 0.5 G 和 0.16 G 条件下，内聚力相互作用的增加限制了通过自由落体的颗粒供应，从而使粉末床层密度分别降低了约 2 % 和 6.2 %。这些发现证明，在低重力条件下的铺展过程中，内聚力主要控制着动态流动性和粉床质量。
{"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 , Hao Wang , Kenta Aoyagi , Kenta Yamanaka , 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}
Pub Date : 2024-08-25DOI: 10.1016/j.addma.2024.104504
Nicolò Bonato, Filippo Zanini, Simone Carmignato
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.
激光粉末床熔融金属越来越多地用于制造需要良好机械性能的复杂零件。与此同时，该领域的研究人员也在加大力度减少缺陷，如内部气孔，这些缺陷阻碍了该技术在工业领域的广泛应用，促使过程监控在缺陷识别和缓解方面发挥关键作用。因此，了解过程中监测信号与过程后实际缺陷之间的相关性，是在过程中做出明智决策和采取潜在纠正措施的基础。这项工作的重点是开发模型，以便根据离轴长曝光成像检测到的特定工艺特征预测与飞溅有关的缺陷。分层图像与断层扫描重建的相应横截面适当对齐，以研究与飞溅相关的特征和 X 射线计算机断层扫描测量的实际缺陷之间的关系。以这种关系为知识基础，开发了一种分析图像处理方法和一种基于机器学习的方法，然后对这两种方法的相关性能进行了比较。本文讨论了这两种方法的优势和局限性。这两种方法在预测由飞溅物引起的熔合不足缺陷方面都取得了很好的结果，机器学习方法对等效直径大于 90 微米的缺陷的预测准确率达到了 90%，而分析模型需要等效直径大于 130 微米才能达到 80%的预测准确率。此外，机器学习方法在早期缺陷检测方面也取得了很好的结果，在特征检测之后，通过分析连续两层，可以正确预测大多数被调查的缺陷。
{"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, Filippo Zanini, 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}
Pub Date : 2024-08-25DOI: 10.1016/j.addma.2024.104478
Patxi Fernandez-Zelaia , Saket Thapliyal , Rangasayee Kannan , Peeyush Nandwana , Yukinori Yamamoto , Andrzej Nycz , Vincent Paquit , Michael M. Kirka
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.
{"title":"Denoising diffusion probabilistic models for generative alloy design","authors":"Patxi Fernandez-Zelaia , Saket Thapliyal , Rangasayee Kannan , Peeyush Nandwana , Yukinori Yamamoto , Andrzej Nycz , Vincent Paquit , 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}
Pub Date : 2024-08-25DOI: 10.1016/j.addma.2024.104490
Taasnim Ahmed Himika , Louise Olsen-Kettle , Dong Ruan , Ali Daliri
<div><div>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 (<span><math><mrow><mi>θ</mi><mo>=</mo><msup><mrow><mn>0</mn></mrow><mrow><mi>o</mi></mrow></msup><mo>,</mo><mspace></mspace><mn>3</mn><msup><mrow><mn>0</mn></mrow><mrow><mi>o</mi></mrow></msup><mo>,</mo><mspace></mspace><mn>4</mn><msup><mrow><mn>5</mn></mrow><mrow><mi>o</mi></mrow></msup><mo>,</mo><mspace></mspace><mn>6</mn><msup><mrow><mn>0</mn></mrow><mrow><mi>o</mi></mrow></msup><mo>,</mo><mspace></mspace><mn>9</mn><msup><mrow><mn>0</mn></mrow><mrow><mi>o</mi></mrow></msup></mrow></math></span>) 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 <span><math><mrow><mn>7</mn><mo>−</mo><mn>8</mn><mtext>%</mtext></mrow></math></span> and increasing the ultimate tensile strength (<span><math><msub><mrow><mi>σ</mi></mrow><mrow><mi>T</mi></mrow></msub></math></span>) by <span><math><mrow><mn>11</mn><mo>−</mo><mn>18</mn><mtext>%</mtext></mrow></math></span>, tensile stiffness (<span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>T</mi></mrow></msub></math></span>) by <span><math><mrow><mn>6</mn><mo>−</mo><mn>8</mn><mtext>%</mtext></mrow></math></span>, and the tensile failure strains (<span><math><msub><mrow><mi>ϵ</mi></mrow><mrow><mi>T</mi><mo>,</mo><mi>f</mi><mi>a</mi><mi>i</mi><mi>l</mi></mrow></msub></math></span>) by <span><math><mrow><mn>1</mn><mo>−</mo><mn>11</mn><mtext>%</mtext></mrow></math></span>. 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
{"title":"Effects of nozzle geometry and fiber orientation on the tensile strength of 3D printed continuous fiber reinforced composites","authors":"Taasnim Ahmed Himika , Louise Olsen-Kettle , Dong Ruan , Ali Daliri","doi":"10.1016/j.addma.2024.104490","DOIUrl":"10.1016/j.addma.2024.104490","url":null,"abstract":"<div><div>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 (<span><math><mrow><mi>θ</mi><mo>=</mo><msup><mrow><mn>0</mn></mrow><mrow><mi>o</mi></mrow></msup><mo>,</mo><mspace></mspace><mn>3</mn><msup><mrow><mn>0</mn></mrow><mrow><mi>o</mi></mrow></msup><mo>,</mo><mspace></mspace><mn>4</mn><msup><mrow><mn>5</mn></mrow><mrow><mi>o</mi></mrow></msup><mo>,</mo><mspace></mspace><mn>6</mn><msup><mrow><mn>0</mn></mrow><mrow><mi>o</mi></mrow></msup><mo>,</mo><mspace></mspace><mn>9</mn><msup><mrow><mn>0</mn></mrow><mrow><mi>o</mi></mrow></msup></mrow></math></span>) 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 <span><math><mrow><mn>7</mn><mo>−</mo><mn>8</mn><mtext>%</mtext></mrow></math></span> and increasing the ultimate tensile strength (<span><math><msub><mrow><mi>σ</mi></mrow><mrow><mi>T</mi></mrow></msub></math></span>) by <span><math><mrow><mn>11</mn><mo>−</mo><mn>18</mn><mtext>%</mtext></mrow></math></span>, tensile stiffness (<span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>T</mi></mrow></msub></math></span>) by <span><math><mrow><mn>6</mn><mo>−</mo><mn>8</mn><mtext>%</mtext></mrow></math></span>, and the tensile failure strains (<span><math><msub><mrow><mi>ϵ</mi></mrow><mrow><mi>T</mi><mo>,</mo><mi>f</mi><mi>a</mi><mi>i</mi><mi>l</mi></mrow></msub></math></span>) by <span><math><mrow><mn>1</mn><mo>−</mo><mn>11</mn><mtext>%</mtext></mrow></math></span>. 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}
Pub Date : 2024-08-25DOI: 10.1016/j.addma.2024.104501
Xiankun Cao , Chenghong Duan , Xiangpeng Luo , Shaopeng Zheng , Hangcheng Xu , Xiaojie Hao , Zhihui Zhang
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.
{"title":"Deep learning-based rapid prediction of temperature field and intelligent control of molten pool during directed energy deposition process","authors":"Xiankun Cao , Chenghong Duan , Xiangpeng Luo , Shaopeng Zheng , Hangcheng Xu , Xiaojie Hao , 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}
Pub Date : 2024-08-25DOI: 10.1016/j.addma.2024.104476
Miguel-Angel Pardo-Vicente , Pablo Pavón-Domínguez , Daniel Moreno-Nieto , Miriam Herrera-Collado
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.
增材制造（AM）已经达到了可靠的成熟水平，特别是熔融长丝制造（FFF），正在成为最普遍的工艺。与此同时，工业对这些零件的需求也在增加，这就要求对其内部几何形状进行分析，以确定与预期结构的相似程度。这项工作旨在通过将印刷特性与几何变量联系起来，提供表征 FFF 零件的工具。为此，我们用聚乳酸（PLA）打印了三种不同层高的样品，并通过 X 射线计算机断层扫描（CT）进行了分析。处理图像后，使用盒计数法对丝线之间出现的空隙进行分形分析，以获得分形维度。同时还计算了空隙的孔隙率。分析确定了空隙的数量、大小、形状和位置等特征参数。与传统的孔隙率研究不同，这项工作的新颖之处在于分形分析以单一数值（分形维度）提供了有关空隙形状和分布的信息。研究证实，分形维度不仅取决于孔隙度，还取决于空隙的形状和位置。此外，研究还发现，并非所有空隙参数都会对几何变量产生同样的影响；与孔隙率（空隙的数量和大小）相关的变量比形状和位置更重要。最后，研究表明，通过了解层高和挤压流参数，可以确定理想的孔隙率和分形尺寸，而与这些参数的任何偏差都表明产生了几何印刷误差。
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Pub Date : 2024-08-25DOI: 10.1016/j.addma.2024.104507
Chao Liu , Yukun Zhang , Huawei Liu , Yiwen Wu , Shiwei Yu , Chuihui He , Zhan Liang
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.
在加固的 3D 打印混凝土结构中，钢筋和 3D 打印丝之间的剪切相互作用会影响混凝土结构在使用过程中的性能和安全性。本研究以层间界面的孔隙结构特征为重点，研究了带有再生粗集料（3DPRAC）的 3D 打印混凝土层间加固界面（IRI）的剪切性能。通过改变锚固钢筋钉（ARN）的数量，提出了一种增强 IRI 的方法，并将结果与天然粗骨料 3D 打印混凝土（3DPRAC）和 3D 打印砂浆（3DPM）的结果进行了比较。结果表明，ARNs 显著提高了 3DPRAC 的 IRI 剪切强度。具体来说，3DPRAC 的 IRI 剪切强度比 3DPNAC 低 6.1%，但比 3DPM 高 43.6%。通过研究螺纹钢-3DPRAC 粘结区域的结构特征，确定了孔隙缺陷与剪切强度之间的关系。根据这一关系，提出了层间结合界面的分区模型。我们开发了一个包含真实孔隙结构特征的 3D 打印混凝土有限元模型，用于分析界面在剪应力作用下的应力分布和破坏特征。此外，还分析了局部孔隙诱发的断裂模式以及裂纹扩展机制。最后，根据虚功原理和塑性极限理论，推导出了计算 3DPRAC IRI 剪切强度的统一公式。该研究为 3D 打印钢筋混凝土结构的工程应用提供了理论支持。
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Pub Date : 2024-08-25DOI: 10.1016/j.addma.2024.104488
Juan-Sebastian Rincon-Tabares , Mauricio Aristizabal , Matthew Balcer , Arturo Montoya , Harry Millwater , David Restrepo
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.
使用激光束进行金属粉末床熔化（PBF-LB/M）过程中出现的快速循环温度波动会影响印刷部件缺陷的形成。因此，迫切需要通过开发创新方法来提高印刷部件的质量，这些方法可以预测热历史并帮助揭示工艺参数与热曲线之间的复杂关系。为此，灵敏度分析（SA）成为必不可少的工具，为优化工艺和加强质量控制提供了可能。然而，传统的敏感性分析方法往往会产生过高的计算成本和潜在的数值近似误差。为了解决这一技术难题，我们提出了一种新的 SA 方法，该方法将基于 HYPercomplex 的自动微分（HYPAD）技术与通过有限元法（FEM）进行的瞬态热模拟相结合。利用这种方法，我们可以在后处理步骤中高效、准确地执行 PBF-LB/M 过程的 SA。与有限差分法（FD）等传统方法相比，在美国国家标准与技术研究院 2018-02 AM 基准的比较研究中，HYPAD-FEM 获取 22 个工艺参数敏感性所需的计算时间减少了 96%。总之，与传统方法相比，HYPAD-FEM 在 SA 方面具有更高的效率和准确性，可提供模型的最佳灵敏度，而无需选择步长和基于问题或参数的实现方法。
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Pub Date : 2024-08-25DOI: 10.1016/j.addma.2024.104469
Junrui Tan , Guizhi Zhu , Longfei Tan , Qiong Wu , Zhixu Liu , Mingwei Yang , Xianwei Meng
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.
材料挤压已彻底改变了具有复杂定制结构的有机硅弹性体的制造。然而，油墨可印刷性与功能性填料复合之间的权衡阻碍了三维打印硅树脂弹性体在电磁干扰（EMI）屏蔽和热管理等应用领域的发展。在本研究中，我们提出了一种制造功能性硅树脂弹性体的新方法，重点关注填料、油墨和结构的设计。改性纳米片实现了油墨的可印刷性，通过在有机硅基体中构建动态界面相互作用，赋予了油墨触变性和自支撑能力。此外，由于其分层结构，改性纳米片在高剪切速率下表现出 "润滑 "效果，从而促进了挤出过程的顺利进行。以周期性多孔结构的电磁干扰屏蔽模拟为指导，我们成功打印出了宽带电磁干扰屏蔽硅树脂弹性体。此外，我们还通过制作定制的三维打印屏蔽盒和可穿戴热管理薄膜，展示了我们的方法的多功能性，展示了三维打印硅树脂弹性体的各种潜在应用。我们预计，我们的创新设计方法将在功能性弹性体和 3D 打印技术之间架起一座桥梁，为它们在各个领域的应用开辟新的途径。
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Pub Date : 2024-08-25DOI: 10.1016/j.addma.2024.104457
Ruiyao Liu , Guofeng Yao , Qingyang Wang , Nuo Yang , Jundong Zhang , Chaolei Zhang , Yuancheng Zhu , Xiang Li , Zhenglei Yu , Yunting Guo , Zezhou Xu , Peng Li , Chunling Mao
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.
{"title":"Stress-adaptive femur bionic triple periodic minimal heterostructures manufactured by SLS technology with excellent mechanical properties","authors":"Ruiyao Liu , Guofeng Yao , Qingyang Wang , Nuo Yang , Jundong Zhang , Chaolei Zhang , Yuancheng Zhu , Xiang Li , Zhenglei Yu , Yunting Guo , Zezhou Xu , Peng Li , 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}
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