Additive manufacturing最新文献

{"title":"Bead geometry prediction in wire arc directed energy deposition using physics-informed machine learning and low-fidelity data","authors":"Asif Rashid,&nbsp;Farzad Vatandoust,&nbsp;Akshar Kota,&nbsp;Shreyes N. Melkote","doi":"10.1016/j.addma.2025.104881","DOIUrl":"10.1016/j.addma.2025.104881","url":null,"abstract":"<div><div>Wire Arc Directed Energy Deposition (Wire Arc DED) is a promising metal additive manufacturing technique, yet accurate bead geometry prediction remains a challenge due to the complex thermal and geometric interactions in the process. In this study, we present a coupled Physics-Informed Neural Network (PINN) framework to predict the bead geometry by integrating the governing process physics and experimental data, thereby addressing the limitations of both computationally expensive numerical models and purely data-driven approaches. The model employs a sequential two-step workflow, where a thermal model first predicts temperature evolution, which subsequently informs a geometry model for predicting the bead geometry. Results indicate that a high-fidelity PINN model with high spatiotemporal resolution captures the intricately coupled thermal and geometric variations inherent to bead deposition with good predictive accuracy albeit at a higher computational cost, while a low-fidelity PINN model with lower spatiotemporal resolution offers a computationally efficient alternative with marginally higher errors. The incorporation of measured bead geometry data significantly enhances prediction accuracy, with a minimal amount of low-fidelity data sufficing to refine predictions effectively. Moreover, the model generalizes well across different bead locations along the deposition length, demonstrating reliable performance. The high-fidelity PINN model, using a temporal step size of 0.2 s, achieves an average height prediction error of 8.38 % and width error of 1.09 % after approximately 12.7 hours of training on four H100 GPUs. In contrast, the low-fidelity model, with a coarser temporal step size of 0.5 s, reaches nearly the same accuracy (8.33 % height error, 1.56 % width error) with just 2.7 h of training on a single H100 GPU. This corresponds to a 79 % reduction in training time and substantially lower hardware requirements, highlighting the scalability and efficiency of the proposed hybrid modeling approach.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104881"},"PeriodicalIF":10.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632424","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":"Laser powder bed fusion vs. single track laser melting of martensitic Ti-Nb: Phase and microstructure formation","authors":"Florian Senftleben ,&nbsp;Mariana Calin ,&nbsp;Jürgen Eckert ,&nbsp;Matthias Bönisch","doi":"10.1016/j.addma.2025.104884","DOIUrl":"10.1016/j.addma.2025.104884","url":null,"abstract":"<div><div>The aim of this work is to explore the fabrication of α″ Ti-Nb via laser powder bed fusion (LPBF) using pre-alloyed ball-milled feedstock powders. Ti-29Nb alloy powder was prepared by mechanical alloying of elemental Ti and Nb powders, using NaCl as milling agent. Pre-alloyed powders were consolidated into bulk cuboids via LPBF and the effect of different build settings on resulting phases, microstructure and porosity was studied. Phases and microstructures of the LPBF parts were compared with those of single tracks on α″ martensitic substrates. Depending on the Nb content, LPBF leads to either planar or cellular-dendritic solidification. α′ and α″ martensites, β and α phase form in the as-built parts. Single track experiments show that planar growth is conducive for the formation of α″ martensite post-solidification. While in-situ alloying is possible for specific LPBF settings, the use of pre-alloyed powders is recommended to enlarge the build parameter space for reproducible as-built microstructures.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104884"},"PeriodicalIF":11.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720954","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":"Uncertainty-driven trustworthy identification paradigm for unstable melt pool state based on acoustic emission in LPBF","authors":"Jiafeng Tang ,&nbsp;Kunpeng Tan ,&nbsp;Junlong Tang ,&nbsp;Zhibin Zhao ,&nbsp;Xingwu Zhang ,&nbsp;Xuefeng Chen","doi":"10.1016/j.addma.2025.104887","DOIUrl":"10.1016/j.addma.2025.104887","url":null,"abstract":"<div><div>Thanks to the high precision and flexibility, laser powder bed fusion (LPBF) has hugged in producing key components for the fields of aerospace and biomedicine. However, ensuring the consistent of quality during the manufacturing process remains a headache challenge. Online monitoring the state of melt pool and implementing related closed-loop feedback control is a promising solution to improve quality stability. Especially, the combination of online monitoring and deep learning (DL)-based methods is gaining significant traction. Unfortunately, the ‘black-box’ nature of DL models reduces their reliability of prediction. Additionally, the complex multiphysics-coupled nature of the melt pool often causes transient fluctuations that manifest the inter-layer and intra-layer heterogeneity in monitoring data, which deepens the credibility crisis of DL methods and closed-loop control. In this work, we propose a reliable paradigm for identifying the unstable state of melt pool over inter-layer and intra-layer in LPBF, MSRIM (<strong>M</strong>elt pool <strong>S</strong>tate <strong>R</strong>eliable <strong>I</strong>dentification <strong>M</strong>odel). It outputs both predictions and their uncertainties, enabling control systems to dynamically adjust strategies based on confidence levels. Concretely, we analyze and investigate the heterogeneity of processing data caused by fluctuations of melt pool under different scenarios, along with the uncertainties introduced by such heterogeneity. Then, we quantify and decompose the uncertainties from different sources, and provides a reliable foundation for online control of quality. Furthermore, we develop a custom LPBF melt pool full-processing acoustic emission (AE) monitoring system and created an AE-based dataset including 36 groups of parameters with three melt pool states for verifying our work. Extensive experiments demonstrate that our paradigm achieves the satisfactory and reliable melt pool state identification.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104887"},"PeriodicalIF":10.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614615","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":"Optimizing rheological properties of 3D printed cementitious materials via ensemble machine learning","authors":"Muhammad Saeed Zafar ,&nbsp;Farid Javadnejad ,&nbsp;Maryam Hojati","doi":"10.1016/j.addma.2025.104889","DOIUrl":"10.1016/j.addma.2025.104889","url":null,"abstract":"<div><div>The complex interaction between rheology-modifying admixtures and fresh cementitious mix printability limits 3D printing applications in construction. To optimize the properties of 3D printable concrete, this study presents a machine learning (ML)-based, knowledge-guided framework that integrates data-driven modeling with expert validation. A structured workflow uses a small dataset to predict and refine optimal mix designs. A total of 77 lab samples were prepared with varying amounts of nano-clay (NC), silica fume (SF), bentonite volclay (BC), and methylcellulose (MC). Their rheological properties, including plastic viscosity (VIS), dynamic yield stress (DYS), and static yield stress (SYS), were measured using a rheometer. Ensemble ML models were developed through automated preprocessing, cross-validated hyperparameter tuning, and RMSE-based selection. The top five models per rheological responses were combined using a voting regressor, improving predictive accuracy while mitigating overfitting. Predictions were visualized using contour maps from gridded synthetic data, revealing nonlinear interactions among input features. A key innovation is applying expert ratings to contour maps to guide the selection of high-performing mixes. This step allows domain knowledge to define acceptable printability ranges and helps address ML uncertainty from limited training data. Optimized mixes were selected based on rating maps and re-evaluated through additional rheology and 3D printing tests. The results demonstrated that the mixes met satisfactory extrudability and buildability requirements, confirming the validity of the defined expert rating criteria and the practical utility of the framework in optimizing 3D printable concrete mixes containing the defined additives. The proposed approach ensures both predictive robustness and practical applicability. It enables iterative refinement of models as new data becomes available and offers a systematic approach to navigating complex mix interactions. Overall, combining ensemble modeling, contour visualization, and knowledge-driven evaluation provides a powerful tool for advancing 3D concrete printing mix design.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104889"},"PeriodicalIF":10.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694569","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":"Ultrasonic rolling-enhanced additive manufacturing of IN718 superalloy: Microstructural refinement and mechanical property improvement through variable power modulation","authors":"Hang Lin ,&nbsp;Zhizhuo Li ,&nbsp;Mingwang Fu ,&nbsp;Hao Yi ,&nbsp;Haiou Zhang ,&nbsp;Runsheng Li","doi":"10.1016/j.addma.2025.104891","DOIUrl":"10.1016/j.addma.2025.104891","url":null,"abstract":"<div><div>Conventional wire and arc direct energy deposition (WADED) of nickel-based superalloys faces critical challenges, such as, coarse columnar grains, pronounced elemental segregation, and suboptimal mechanical performance, hindering their applications in high-value aerospace industries. Herein, we developed an ultrasonic rolling-assisted WADED (UR-WADED) strategy that synergistically couples dynamic plastic deformation with in-situ ultrasonic vibration. Through systematic modulation of ultrasonic power (0–90 %), its effects on dendritic evolution, phase transformation, and dislocation dynamics were decoupled. Multiscale characterization revealed that ultrasonic mechanical excitation induced three key effects: (1) grain refinement was achieved through the combined effects of acoustic cavitation and rolling. Under high-power UR, a mixed grain structure was formed, and the average grain size in the fine-grained region was reduced by 80.5 % (from 178.68 μm to 34.87 μm); (2) The joint action of acoustic streaming and rolling transformed the morphology of the Laves phase from a continuous chain-like distribution into a more dispersed island-like form; (3) Texture randomization occurred, with the maximum intensity of the (001) pole figure reduced by 62 %, accompanied by the generation of a high density of intragranular dislocations. The optimized 90UR-WADED specimen exhibited significant property enhancement: Vickers hardness increased by 42.5 % (376.2 vs 264.1 HV_0.5), while yield and ultimate tensile strengths surged to 768.2 (+55.5 %) and 1072.9 MPa (+38.9 %), respectively, outperforming conventional WADED counterparts. Quantitative strengthening analysis identified grain boundary strengthening (∼59 %) and dislocation hardening (∼23 %) as dominant mechanisms. After heat treatment, the 90UR-WADED sample exhibited a fully equiaxed grain structure, and its mechanical properties surpassed those of wrought IN718. This work established a notable hybrid manufacturing approach that overcomes the intrinsic limitations of arc-based additive manufacturing and provides a scalable pathway for fabricating high-performance superalloy components.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104891"},"PeriodicalIF":10.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679835","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":"Enhanced strength with retained ductility in SLM-processed high-entropy alloys via dislocation regulation in L2₁-BCC co-precipitate","authors":"Xue Li ,&nbsp;Xiaoqiang Wang ,&nbsp;Xianglong Dai ,&nbsp;Yi Li ,&nbsp;Yan Zhou ,&nbsp;Yuan Wu ,&nbsp;Xinjian Yuan ,&nbsp;Shifeng Wen ,&nbsp;Yusheng Shi","doi":"10.1016/j.addma.2025.104899","DOIUrl":"10.1016/j.addma.2025.104899","url":null,"abstract":"<div><div>L2₁-BCC co-precipitates were formed in a Fe-Co-Ni-Cr-Al-Ti high-entropy alloy fabricated via selective laser melting, followed by a specific heat treatment process. Two types of co-precipitates were identified based on the scale of the BCC phase, with both exhibiting fully coherent interfaces. For the first time, the dynamic interaction mechanism between co-precipitates and dislocations was revealed through in-situ transmission electron microscope. First, the cross-slip of dislocations occurred, promoting uniform deformation within the co-precipitates. Additionally, back stress exerted by the BCC phase facilitated the activation of slip systems in the L2₁ phase. The dislocation interaction with the L2₁ phase shifted from conventional bypassing to cutting, mitigating stress concentration at the FCC/L2₁ phase boundary. Consequently, a high-density dislocation zone formed in the L2₁ phase near the interface, which not only prevented dislocation pile-up but also promoted cross-slip. Finally, a cracking prevention mechanism associated with the gradient precipitation phase was identified. The co-precipitate structure achieved a remarkable 73.6 % enhancement (813 MPa to 1411 MPa) in ultimate tensile strength compared to the single L2₁ precipitate system while maintaining considerable ductility. These findings provide a foundation for the development of multiphase structural designs in high-entropy alloys and a breakthrough in the strength-ductility trade-off.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104899"},"PeriodicalIF":11.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724950","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":"Multiphysics modelling of 3D concrete printing: From material model to process simulation and optimisation","authors":"Maxime Pierre ,&nbsp;Siavash Ghabezloo ,&nbsp;Patrick Dangla ,&nbsp;Romain Mesnil ,&nbsp;Matthieu Vandamme ,&nbsp;Jean-François Caron","doi":"10.1016/j.addma.2025.104847","DOIUrl":"10.1016/j.addma.2025.104847","url":null,"abstract":"<div><div>Predictive simulation of 3D concrete printing is important to warrant printability and durability of print pieces and to optimise printing parameters, yet tedious due to the complexity of the material behaviour and printing process. From a constitutive model allowing a continuous description of the coupled chemo-thermo-poro-mechanical behaviour of cement-based materials from the early-age to the hardened state, a comprehensive finite element simulation framework is designed. It aims at modelling extrusion-based 3D printing processes, taking into account the sequential deposition of material. Study of the onset of plastic collapse on specific geometries at different printing speeds show the complexity of collapse prediction as well as the importance of process-related effects. An optimisation scheme is proposed to determine optimal printing speed modulations from numerical simulations with the perspective of increasing productivity in 3D concrete printing. The model shows good predicting capabilities when compared with experimental printing failures, and is able to extrapolate to other accelerator dosages without model re-calibration.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104847"},"PeriodicalIF":10.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518602","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":"Scalable path planning and reduced order modeling for temperature optimization in Direct Energy Deposition","authors":"Iason Sideris ,&nbsp;Yiyang Yan ,&nbsp;Stephen Duncan ,&nbsp;Mohamadreza Afrasiabi ,&nbsp;Markus Bambach","doi":"10.1016/j.addma.2025.104831","DOIUrl":"10.1016/j.addma.2025.104831","url":null,"abstract":"<div><div>Direct energy deposition (DED) processes, including laser DED and wire-arc additive manufacturing, provide high throughput and geometric flexibility, yet dimensional inaccuracies and heterogeneous properties frequently arise when sub-optimal tool paths create uneven temperature fields. Thermally aware path optimization is therefore essential but remains computationally prohibitive for complex geometries, forming the principal bottleneck in current algorithms. This study introduces an efficient planning framework that constructs a reduced order thermal model with GPyro, a machine-learning subspace technique that predicts temperature profiles only on the deposition layer. This allows swift layer-wise reductions, thereby extending the applicability of reduced-order models to arbitrary three-dimensional geometries. Additionally, the algorithm leverages the fast Fourier transform to evaluate temperature evolution efficiently, significantly reducing computational time while preserving accuracy. Compared to existing methods, the proposed approach achieves up to a 10<span><math><msup><mrow></mrow><mrow><mn>9</mn></mrow></msup></math></span>-fold reduction in pre-computing time and a 10<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>-fold acceleration in evaluating process temperature fields. Experimental validation on components with high overhang angles confirms the effectiveness of the algorithm, consistently producing high-quality, defect-free parts and demonstrating that coupling GPyro with iterative optimizers enables the optimization of deposition strategies, even for complex geometries.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104831"},"PeriodicalIF":10.3,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511032","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":"Concurrent optimization of building direction and structural topology for multi-axis additive manufacturing of rotary parts considering anisotropic strength","authors":"Cheng Yan ,&nbsp;Haowei Guo ,&nbsp;Ben Pei ,&nbsp;He Liu ,&nbsp;Yun Chen ,&nbsp;Cunfu Wang ,&nbsp;Zeyong Yin","doi":"10.1016/j.addma.2025.104851","DOIUrl":"10.1016/j.addma.2025.104851","url":null,"abstract":"<div><div>The additional printing degrees of freedom in multi-axis additive manufacturing (AM) based on the BC table machine facilitate control of the building direction (BD), the formation of complex curved surfaces, and the fabrication of rotary parts. This provides significant advantages in controlling material anisotropy and structural layouts. In topology optimization (TO), concurrent optimization of BD and topological layouts can fully take advantage of the process-induced anisotropy. However, most of the previous studies were limited to three-axis AM systems and failed to fully exploit the manufacturing potential of multi-axis AM machines. Therefore, this study develops a TO method tailored for multi-axis AM based on the BC table machine. Firstly, an innovative constitutive model is developed for printing rotary parts based on the BC table machine. This model describes the constitutive characteristics of anisotropic rotary parts formed by <span><math><mi>C</mi></math></span>-axis rotation after adjusting the print platform based on the <span><math><mi>B</mi></math></span>-axis to a non-horizontal plane, providing a theoretical foundation for material property interpolation and BD optimization. Secondly, the Tsai–Hill failure criterion for multi-axis AM of rotary parts is derived, which can predict the anisotropic strength distribution under different BDs. Next, a TO model is developed to concurrently optimize BD and topological layouts considering anisotropic strength and structural stiffness in multi-axis AM, and the sensitivities of the objective function and constraints are derived. Finally, optimization examples of hook supports and compressor disks are presented to validate the importance and effectiveness of BD optimization and anisotropic strength constraints, while an optimization example of turbine rear cooling plates demonstrates the method’s engineering applicability. The results show that this method can concurrently optimize the BD and structural layout of multi-axis AM of rotary parts, fully utilizing anisotropy in AM and improving overall structural performance.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104851"},"PeriodicalIF":10.3,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502538","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":"Fabrication of ophthalmic lenses by Fluidic Shaping","authors":"Mor Elgarisi ,&nbsp;Omer Luria ,&nbsp;Yotam Katzman ,&nbsp;Daniel Widerker ,&nbsp;Valeri Frumkin ,&nbsp;Moran Bercovici","doi":"10.1016/j.addma.2025.104871","DOIUrl":"10.1016/j.addma.2025.104871","url":null,"abstract":"<div><div>Limited access to corrective eyewear remains a significant medical, societal, and economic challenge in developing countries, with more than 1 billion people suffering from uncorrected vision impairment. Philanthropy has failed to meet the demand, and local manufacturing using standard technologies remains beyond reach due to inadequate resources. We present a fluidic approach, leveraging the surface tension of liquid polymers, with which high-quality solid lenses, with any prescription, can be created without machining, polishing or any post-processing steps. We provide an experimentally-validated analytical model relating the geometrical degrees of freedom to the desired prescription. Using a compact low-power device, we demonstrate that the fluidic approach allows the fabrication of industry-standard eyeglasses in several minutes, opening the door to advanced manufacturing in low-resource settings.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104871"},"PeriodicalIF":10.3,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518518","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}