Additive manufacturing最新文献

{"title":"High-speed 3D printing of alloy architectures enabled by continuous liquid interface production","authors":"Dylan Joralmon,&nbsp;Vaibhav Mistari,&nbsp;August Bant,&nbsp;Soham Khairnar,&nbsp;Amir Danial Azimiand,&nbsp;Xiangjia Li","doi":"10.1016/j.addma.2026.105092","DOIUrl":"10.1016/j.addma.2026.105092","url":null,"abstract":"<div><div>Metal additive manufacturing (MAM) has received a lot of research attention since high-quality alloy components with optimized designs can be produced for a wide range of applications in aerospace and biomedical industries. However, the printing resolution, high operational cost, and overall printing time hinder the full potential of MAM as a solution for realizing next generation functionally graded multi-metals. Herein, a low viscosity copper nickel (CuNi) alloy precursor resin was incorporated into the continuous liquid interface production (CLIP) 3D printing process resulting in microscale CuNi alloy objects with low surface roughness (R_a = 0.795 µm) and superior printing speed (exceeding 130 µm/s). Because alloy precursor additives are used to synthesize CuNi alloy parts, physical properties, such as relative density and microstructure porosity, can be controlled by adjusting the CuNi composition, sintering temperature, and metal precursor concentration resulting in microporosity values ranging from extremely dense to highly porous (0.2 %-5.71 %). Moreover, continuously 3D printed CuNi alloys are monolithic and exhibit a uniform microstructure after post-heat treatment, showing an overall volumetric shrinkage of 60–75 %, resulting in isotropic physical properties displayed in the final printed part. Due to the high level of control over the process, novel alloy metamaterials such as bio-inspired lattice structures with microscale porosity and good mechanical or thermal properties can be easily reproduced. This research demonstrates that alloy and multi-metal 3D objects fabricated through the layer-less AM approach provides a cost-effective and innovative strategy to overcome the current limitations of layer-based multi-metal AM technologies.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"118 ","pages":"Article 105092"},"PeriodicalIF":11.1,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076119","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":"Corrigendum to “Formation of compositionally graded grains and molten-salt corrosion behavior in wire-arc additive manufactured NiMoCr alloy-cladded steel” [Addit. Manuf. 116 (2026), 105079]","authors":"Hanliang Zhu ,&nbsp;Zhijun Qiu ,&nbsp;Zhiyang Wang ,&nbsp;Ondrej Muránsky ,&nbsp;Inna Karatchevtseva ,&nbsp;Huijun Li","doi":"10.1016/j.addma.2026.105084","DOIUrl":"10.1016/j.addma.2026.105084","url":null,"abstract":"","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"116 ","pages":"Article 105084"},"PeriodicalIF":11.1,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034897","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":"Acoustic emission directionality of the melt pool in laser powder bed fusion additive manufacturing","authors":"Shivam Shukla ,&nbsp;Rik Vaerenberg ,&nbsp;Milad Hamidi Nasab ,&nbsp;Konstantinos Gryllias ,&nbsp;Bey Vrancken","doi":"10.1016/j.addma.2026.105096","DOIUrl":"10.1016/j.addma.2026.105096","url":null,"abstract":"<div><div>Vibro-acoustic monitoring has emerged as a promising approach for process monitoring and control in laser powder bed fusion (LPBF). Its high temporal resolution and capability to capture events occurring on the microsecond scale can assist in developing a comprehensive understanding of complex melt pool dynamics and laser-material interaction. However, the physical linkage between the acoustic signal and the process remains a challenge. This work attempts to uncover the complex mechanism of the LPBF process by understanding acoustic signals and linking them with the actual physical conditions. A microphone is used to capture the acoustic emissions (AE) generated from the processing zone. Additionally, an on-axis high-speed camera is used for melt pool monitoring and spatter tracking. Analyzing AE signals shows that signal energy fluctuations within the frequency band of 100–250 kHz are influenced by changes in inherent processing conditions, such as scan direction and the orientation of the vector sequence direction. These fluctuations are correlated with backward vapor plume, melt pool asymmetry, and spatter ejection during layer processing. The study further reveals that during processing, the spatter ejects toward the built side of the layer. As most of the previous work on the use of acoustic emission monitoring of laser powder bed fusion relied heavily on machine learning (ML) for regime classification and anomaly detection, correlating acoustic signals with the actual physical events will add to data labeling by providing complementary contextual information, thereby enhancing the performance of these models. Additionally, this contextual information is a prerequisite to be able to advance acoustic monitoring from the layer level to the more local defect level.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"118 ","pages":"Article 105096"},"PeriodicalIF":11.1,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076183","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":"Preparation and precision control of alumina-based ceramic core using the SLA-Micromilling hybrid process","authors":"Hongyu Xing ,&nbsp;Wenxuan Jiang ,&nbsp;Xinfeng Wang ,&nbsp;Laixiao Lu ,&nbsp;Zhenzhong Zhang ,&nbsp;Guangchao Hao ,&nbsp;Yanhua Zhao ,&nbsp;Bin Zou ,&nbsp;Chuanzhen Huang","doi":"10.1016/j.addma.2026.105095","DOIUrl":"10.1016/j.addma.2026.105095","url":null,"abstract":"<div><div>SLA-3D printing technology offers significant advantages in the fabrication of complex ceramic cores. However, several issues must be addressed immediately, which include dimensional deviations caused by light scattering in ceramic-resin slurries, deformation resulting from anisotropic shrinkage during sintering, and surface quality limitations caused by interlayer step effects. To address these issues, a SLA-Micromilling hybrid manufacturing process for preparing alumina-based ceramic cores is proposed, which consists of three stages: SLA-3D printing of the green body, micro-milling refinement of the green body, and debinding sintering. Firstly, a KH570-H/Al₂O₃ ceramic slurry suitable for SLA-3D printing was designed and prepared, capable of being transformed into SiO₂/Al₂O₃ ceramic. During high-temperature sintering, this slurry forms rod-like mullite phases, which improve mechanical properties through a whisker-toughening mechanism. At a sintering temperature of 1400 ℃, the material exhibits a porosity of 33.7 % and a flexural strength of 28.7 MPa. The shrinkage rates in the x, y, and z directions were 10.4 %, 9.8 %, and 13.7 %, respectively. Secondly, to address dimensional deviations caused by the size sensitivity of SLA-3D printed green bodies and sintering anisotropy, a segmented compensation model was established, reducing the deviation rate in both the printing and sintering stages to approximately 5 %. Micro-milling technology was introduced to enhance machining precision even further. A systematic investigation of the material removal mechanisms during micro-milling of green bodies was conducted, including the effects of resin softening and adhesion, as well as ceramic particle attachment on tool wear. Optimizing cutting parameters (a_p=0.3 mm, n = 1100 r/min, f=75 mm/min) resulted in the blank’s surface roughness Ra of 0.3018 μm. Finally, alumina-based complex core samples fabricated using a composite process of SLA printing, micro-milling, and sintering resulted in a significant reduction of surface roughness from 3.571 μm to 0.3113 μm, with a dimensional standard deviation of 0.0588 mm. And the thermal shock resistance, high-temperature creep resistance, and high-temperature chemical stability of ceramic core samples were evaluated.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"118 ","pages":"Article 105095"},"PeriodicalIF":11.1,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076118","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":"STEAM: A scalable data-driven surrogate modeling framework for part-scale scanwise thermal process simulation of laser powder bed fusion","authors":"Berkay Bostan,&nbsp;Praveen Vulimiri,&nbsp;Shawn Hinnebusch,&nbsp;Dhruba Aryal,&nbsp;Albert C. To","doi":"10.1016/j.addma.2026.105090","DOIUrl":"10.1016/j.addma.2026.105090","url":null,"abstract":"<div><div>Simulating scanwise thermal behavior at part scale in laser powder bed fusion (LPBF) is essential for understanding defects and optimizing process parameters, but it remains prohibitively expensive for real engineering parts. While finite element method (FEM) based process simulations provide valuable insights, the steep computational cost limits their routine application. This work introduces a scalable data-driven surrogate modeling framework for scanwise LPBF thermal simulation through deep learning called STEAM (Scanwise Thermal Emulator for Additive Manufacturing) to predict the transient thermal history of complex parts. The key features behind the proposed framework include 1) a compact dataset constructed from geometrically distinct modular blocks capturing representative thermal patterns, and 2) a combination of temporal and spatial features employed as model inputs to capture key physical phenomena influencing thermal behavior. Temporal features describe how each node interacts with the moving heat source over time, while spatial features capture the effect of the surrounding geometry on local heat conduction and boundary conditions. The model is validated on 2.5D test geometries as much as ∼10 times larger than the training cases with different geometric complexity. On large-scale test blocks, STEAM achieves an <em>R²</em> of 0.97, and 29.61 °C mean absolute error, with a 1237–3648 × speedup compared with FEM using four GPUs (i.e., 309–912 × per GPU). Importantly, performance improves with increasing model complexity, which is promising for further scaling STEAM to larger and more complex LPBF domains. These results demonstrate that the STEAM framework provides a promising path toward fast and accurate thermal prediction in real-world LPBF parts.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"118 ","pages":"Article 105090"},"PeriodicalIF":11.1,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076120","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":"Process-aware topology optimization leveraging deep learning surrogate model-based functions for laser powder bed fusion","authors":"Praveen S. Vulimiri,&nbsp;Shane Garner,&nbsp;Albert C. To","doi":"10.1016/j.addma.2026.105094","DOIUrl":"10.1016/j.addma.2026.105094","url":null,"abstract":"<div><div>Additive manufacturing (AM) methods have greatly matured over the years to produce complex metal parts not previously possible. One of the major methods is laser powder bed fusion (LPBF) AM, which melts and sinters powder in a layer-by-layer manner to build a part. However, during the process, the laser melting of the metal powders introduces thermal stresses that distort the part, possibly causing recoater blade interference and build failure. Topology optimization (TO) approaches have been developed to control the residual stress and distortion with geometric constraints and simplified process simulations using the finite element analysis (FEA) method. Due to computational complexity, these works ignore inelastic deformations and material nonlinearities. Indeed, early attempts incorporating these effects limit themselves to support regions or small domains for the same reason. This work enables the first computationally tractable, support-free part TO of residual inelastic deformations due to LPBF manufacturing and end-use application simultaneously. The framework leverages a data-driven surrogate model trained on elastoplastic FEA layerwise modified inherent strain simulations for stainless steel 316L. The residual deformations and sensitivities can be calculated in seconds, easily coupling with the FEA-based end-use application and analytic maximum volume constraint. A multi-objective, discrete, gradient-based TO analysis minimizes the top surface vertical distortion at each layer alongside the end-use application, scaling to hundreds of thousands of design variables. In fact, the largest test example with over 400 iterations converged in less time than the single verification FEA simulation, 2.5 days versus 4 days. Two test examples with various overhang angle constraints are computationally verified and experimentally validated. Notably, the process-aware designs replace convex openings with columnar or thin wall supports and concave openings with teardrop-like openings. In both simulation and experiment, the process-aware designs distorted less, protruded less above the powder layer, and caused zero build failures.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"118 ","pages":"Article 105094"},"PeriodicalIF":11.1,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076184","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":"Integrating 3D-printed metal and polymer geometries with CMOS microdevices toward MEMS high-resolution lateral force sensing","authors":"Isha Lodhi ,&nbsp;Devin K. Brown ,&nbsp;Durga Gajula ,&nbsp;B.D. Haile ,&nbsp;David R. Myers ,&nbsp;Wilbur A. Lam ,&nbsp;Azadeh Ansari","doi":"10.1016/j.addma.2026.105085","DOIUrl":"10.1016/j.addma.2026.105085","url":null,"abstract":"<div><div>Advances in metal 3D-microprinting technologies, such as electrodeposition, have meant that Micro-Electro-Mechanical Systems (MEMS) are no longer limited to soft, non-conductive polymers only. However, challenges such as process compatibility, limited optical resolution (and hence alignment), is why metal micro-additive processes have not been integrated with active CMOS circuitry or microdevices. This work demonstrates methods for integrating high-aspect-ratio (HAR) 3D-printed metal pillars with piezoresistive microdevices to produce compact, high-sensitivity MEMS lateral force sensors. Building on our prior demonstration of the first MEMS device made using traditionally (CMOS) fabricated, functional microdevices with 3D-printed metal, we describe the chip and device level modifications necessary for post-CMOS metal 3D-printing using the aqueous and acidic copper electrodeposition workflow. By using surface height mapping of 2.5D alignment marks patterned adjacent to the sub-micron width piezoresistors, we achieve repeatable print-to-microdevice alignment accuracy better than &lt; ±0.5 μm across chips. The resulting sensors pair N-type, silicon-doped piezoresistors with electrodeposited copper pillars (aspect ratio &gt;70). Electrical measurements confirm device integrity after electrodeposition, while electro-mechanical characterization of the force sensors yield sensitivities up to ΔR/R0 = 0.26% μN⁻¹, and a noise-limited resolution of ±35 nN. The electrodeposited HAR copper pillar sensors exhibit excellent linearity, repeatability, and high resistance to delamination due to the metal-to-metal bonding between the copper pillars and underlying conductive traces. In a second study separate to metal 3D-printing, we boost the MEMS force sensor sensitivity with introduction of stress-concentrating, “open” trenches around the silicon piezoresistors. This approach was tested with two-photon polymerized pillars. The polymer pillar sensors, with open trenches, demonstrate sensitivities up to 0.44% μN⁻¹, and a corresponding higher force resolution of ±20 nN. These sensors also show excellent repeatability and low hysteresis but delaminate off of the silicon devices near Fx ≈ 6 μN. Given that both sensor types use pillar-based, out-of-plane mechanics, the active on-chip footprint is confined to a few μm², making them well suited for high spatial resolution force mapping and forming dense arrays for biological cell/tissue mechanics.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"118 ","pages":"Article 105085"},"PeriodicalIF":11.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076185","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":"Rapid layerwise process model calibration through a surrogate model for interpass temperature history in laser powder bed fusion","authors":"Shawn Hinnebusch ,&nbsp;Praveen S. Vulimiri ,&nbsp;Christapher G. Lang ,&nbsp;Alaa Olleak ,&nbsp;Florian X. Dugast ,&nbsp;Albert C. To","doi":"10.1016/j.addma.2026.105091","DOIUrl":"10.1016/j.addma.2026.105091","url":null,"abstract":"<div><div>Layerwise thermal process simulation is widely used for predicting heat buildup and residual stress in parts built by the laser powder bed fusion (LPBF) process. However, calibration of layerwise process simulation model parameters (such as absorptivity and convection coefficients) using in-process thermal measurements typically requires hundreds of process simulations to reach convergence due to the large model parameter space. To significantly reduce the number of process simulations needed for calibration, this work proposes a surrogate model (SGM) to accurately model the relationship between simulated interpass temperature time history and model parameters needing calibration using simple functions. Specifically, the SGM is constructed by fitting multi-variate polynomials to the temperature histories mapped to a low-dimensional model space through the principal component analysis (PCA), as a function of the process model parameters. Since the SGM is 4–5 orders of magnitude faster than finite element method based process simulations, it can be used to calibrate the model parameters in several minutes. The SGM specifically developed for calibrating three process model parameters requires only ten process simulations to fit its coefficients and is tested to be accurate using 18 simulated temperature histories to fit ten model coefficients, yielding a mean absolute percentage error (MAPE) between 0.12 % and 2.04 % across four test geometries. Applying the fitted surrogate models to calibrate absorptivity, top surface convection coefficient, and part-side surface convection coefficient to match the temperature histories measured by an infrared camera, a MAPE between 2.84 % and 3.46 % is achieved across the four test geometries using the same set of model parameters. Two unseen geometries are tested with the fully calibrated FEM model with errors under 3.7 % MAPE. The proposed SGM approach is demonstrated to be a valuable tool for accurate part-scale thermal prediction by significantly reducing calibration time and computational resources.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"118 ","pages":"Article 105091"},"PeriodicalIF":11.1,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025670","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":"Direct Ink Writing of metakaolin-based geopolymers: Rheology and printability control using PEG and inorganic fillers","authors":"Xavier Lacambra-Andreu ,&nbsp;Mario Scheel ,&nbsp;Arnaud Poulesquen","doi":"10.1016/j.addma.2026.105086","DOIUrl":"10.1016/j.addma.2026.105086","url":null,"abstract":"<div><div>This study aims to optimize the rheological properties of metakaolin (MK) based geopolymers for Direct Ink Writing (DIW) applications. The research explores the effects of Polyethylene Glycol (PEG) and calcite (HP) as rheological modifiers, focusing on their impact on yield stress, shear modulus, the time-dependent evolution of viscosity and structural stability. Through rheological analysis, we identify the formulation with the optimal balance between buildability and extrudability. Results demonstrate that PEG significantly decreases geopolymerization kinetics, increasing the printability window, while HP enhances the mechanical stiffness and yield stress of geopolymer paste. A comprehensive rheological evaluation, including stress growth, oscillatory tests, and creep experiments, highlights the importance of following long-term behavior. A case study on column printing demonstrates the importance of rheological controlling by predicting and confirming critical failure heights (sagging and buckling). X-ray tomography confirms the internal porosity and structural integrity of the printed filters. This work establishes the potential for geopolymer formulations in innovative applications like 3D-printed filtration systems by offering an approach to create formulations that combine printability, shape stability, and mechanical integrity.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"117 ","pages":"Article 105086"},"PeriodicalIF":11.1,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025210","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":"Accelerating laser ray tracing in high fidelity physics simulations of laser melting using squeeze U-net","authors":"Robert C. Blake,&nbsp;Saad A. Khairallah","doi":"10.1016/j.addma.2026.105087","DOIUrl":"10.1016/j.addma.2026.105087","url":null,"abstract":"<div><div>Laser melting is a core component of the ongoing industrial revolution, dubbed Industry 4.0, as lasers facilitate fast and precise melting and fusion in advanced manufacturing. There is a strong need to optimize the laser process using simulations. However, this has proven challenging as high fidelity simulations are needed for predictive modeling and this is currently prohibitively expensive even when run on hundreds of processors on high performance computers. The challenge is capturing complex physics of laser material interaction, fluid dynamics, thermal physics and material phase transformations at various length and time scales. To close this technological gap, we modified a squeeze U-net to accelerate the laser ray tracing component of such high fidelity models by <span><math><mo>∼</mo></math></span>4x–40x while preserving the core physics principle of conservation of energy with 97% accuracy. This approach enables the accurate modeling of global laser energy absorption as a function of local surface temperatures and complex surface topologies, which govern the reflection directions and energy losses of laser rays upon interacting with the material surface.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"118 ","pages":"Article 105087"},"PeriodicalIF":11.1,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006637","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}