International Journal of Lightweight Materials and Manufacture最新文献

{"title":"Laser powder bed fusion of AlN and ZrN reinforced AlSi10Mg matrix composites: Effect of wettability and volume fraction on microstructure and mechanical properties","authors":"V.S. Suvorova ,&nbsp;L.V. Fedorenko ,&nbsp;S.N. Zhevnenko ,&nbsp;B.O. Zotov ,&nbsp;V.Yu. Egorov ,&nbsp;D.D. Zherebtsov ,&nbsp;D.S. Suvorov ,&nbsp;B.B. Khaydarov ,&nbsp;K.Yu. Kotyakova ,&nbsp;A.A. Nepapushev ,&nbsp;I.A. Kovalev ,&nbsp;D.O. Moskovskikh ,&nbsp;S.V. Chernyshikhin","doi":"10.1016/j.ijlmm.2025.04.002","DOIUrl":"10.1016/j.ijlmm.2025.04.002","url":null,"abstract":"<div><div>In this study, Laser Powder Bed Fusion (LPBF) technique was employed to obtain AlN- and ZrN-reinforced AlSi10Mg composites (De Brouckère diameter D[4,3] equals ∼2 μm). The wettability of AlN and ZrN by pure Al and AlSi10Mg melts was investigated, and the phase composition and microstructure of the bulk composites, as well as the hardness and tensile strength, were studied. The impact of wetting on the mechanical properties was also analyzed. The experimental results indicated that ZrN forms a strong interphase bond with Al as a result of reactive wetting. Due to the in-situ reaction, intermetallic inclusions of Zr(Al,Si)₃ were formed, which further strengthened the matrix. Accordingly, small amounts of ZrN (up to 1 vol%) increase the microhardness of AlSi10Mg from 108 to 126 HV_0.1 and the tensile strength from 410 to 448 MPa. In turn, insufficient inert wetting due to the short contact time of the melt during the LPBF process leads to the formation of gaps at the Al/AlN interphase boundary. This phenomenon, as well as the uneven coarsening of Si, results in a decrease in the strength of AlSi10Mg and an insignificant increase in microhardness regardless of the volume fraction of AlN. The obtained results contribute to the understanding of the role of wetting in LPBFed aluminum matrix composites, and also establish the foundation for further experimental and fundamental research in this area.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 4","pages":"Pages 469-482"},"PeriodicalIF":0.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based applications in metal additive manufacturing processes: Challenges and opportunities–A review","authors":"Tuğrul Özel","doi":"10.1016/j.ijlmm.2025.04.001","DOIUrl":"10.1016/j.ijlmm.2025.04.001","url":null,"abstract":"<div><div>In metal additive manufacturing (AM), parts often exhibit quality variations, defects, intricate surface topography, and anisotropic properties influenced by factors such as process parameters, energy and fusion interactions, and material physics. These complexities make metal-AM processes challenging to manage consistently, leading to unacceptable levels of inconsistency. To address these issues and predict quality, in-situ process sensing and monitoring as well as post-process measurements are commonly employed, aiming to enhance process understanding, control, and reliability. This review paper surveys literature on deep learning (DL) methods used in AM processes, discussing current research challenges and future directions. The ultimate objective is to develop intelligent AM systems capable of using real-time process data for automated control decisions and interventions, advancing towards more reliable defect-free manufacturing outcomes.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 4","pages":"Pages 453-468"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrical discharge machining: Recent advances and future trends in modeling, optimization, and sustainability","authors":"Muhamad Taufik Ulhakim ,&nbsp;Sukarman ,&nbsp;Khoirudin ,&nbsp;Dodi Mulyadi ,&nbsp;Hendri Susilo ,&nbsp;Rohman ,&nbsp;Muji Setiyo","doi":"10.1016/j.ijlmm.2025.03.006","DOIUrl":"10.1016/j.ijlmm.2025.03.006","url":null,"abstract":"<div><div>Electrical Discharge Machining (EDM) has experienced significant advancements in modeling, optimization, and sustainability, reflecting the growing demand for intelligent and environmentally friendly manufacturing practices. Advanced modeling techniques, such as finite element analysis (FEA) and artificial intelligence (AI)-driven simulations, have improved the accuracy of process predictions, enabling real-time adjustments and precise control of machining parameters. Optimization approaches, including machine learning-based algorithms, multi-objective optimization, and hybrid methods, have enhanced key performance indicators, such as material removal rate (MRR), surface quality, and tool wear, thereby increasing process efficiency and reducing machining time. The incorporation of AI and machine learning is crucial for addressing EDM challenges and driving future development. Moreover, sustainability has become a key area of emphasis in EDM research, with recent advancements focusing on energy-saving discharge techniques, eco-friendly dielectric fluids, and sustainable waste management practices. The progress made is in line with the Sustainable Development Goals (SDGs), ensuring that EDM contributes to advanced manufacturing while minimizing environmental impact. Future studies should focus on the effects of AI-driven approaches on environmentally friendly EDM practices by prioritizing green dielectrics, energy-efficient machining, and waste reduction strategies. This review highlights the interconnected roles of modeling, optimization, and sustainability in advancing EDM and outlines key research directions to address the remaining challenges.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 4","pages":"Pages 495-511"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Additively manufactured injection mould tooling incorporating gradient density lattice structures for mass and energy reduction","authors":"Rokas Šakalys ,&nbsp;Christopher O'Hara ,&nbsp;Mandana Kariminejad ,&nbsp;Albert Weinert ,&nbsp;Mohammadreza Kadivar ,&nbsp;Bruno Zluhan ,&nbsp;Karl Costello ,&nbsp;Marion McAfee ,&nbsp;Gerard McGranaghan ,&nbsp;Ramesh Raghavendra ,&nbsp;David Tormey","doi":"10.1016/j.ijlmm.2025.03.007","DOIUrl":"10.1016/j.ijlmm.2025.03.007","url":null,"abstract":"<div><div>The benefits of reducing the mass of injection moulding (IM) tooling include opportunities to also reduce material and energy consumption of the Additive Manufacturing L-PBF (Laser Powder Bed Fusion) processes, leading to lower overall costs for the IM setup. This provides a competitive advantage and reduces the environmental impact of the tool-making process in comparison to manufacturing heavier IM tooling. Mass reduction of tooling by using complex internal geometries like lattice structures, which are impossible to achieve using subtractive fabrication approaches, can be easily implemented through additive manufacturing (AM). Therefore, this research exploits the combination of lattice structure design and AM to make functional IM tooling. A tooling design with solid infill was initially modified with a lattice structure of uniform strut thickness, and then Finite Element (FE) Structural Analysis was performed to estimate the stress field typical of an injection mould cycle. Based on these results, a field-driven approach was further applied to alter the lattice structure into a variable gradient strut thickness lattice, aiming for an additional mass reduction. The tooling was additively manufactured using L-PBF technology and successfully applied in the IM process. Mass reductions of 21.86 and 23.95 % were achieved for moving and fixed halves respectively; this corresponds to laser energy savings of 11.06 and 13.44 %. The tooling demonstrated complete functionality during the industrial IM process producing parts within the design specification.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 4","pages":"Pages 522-536"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finite element analysis and experimental whiffletree testing of a small UAV composite wing","authors":"Aryandi Marta ,&nbsp;Fajar Ari Wandono ,&nbsp;Abian Nurrohmad ,&nbsp;Riki Ardiansyah ,&nbsp;Ilham Bagus Wiranto ,&nbsp;Iqbal Reza Alfikri ,&nbsp;Aditya Rio Prabowo ,&nbsp;Gesang Nugroho","doi":"10.1016/j.ijlmm.2025.03.004","DOIUrl":"10.1016/j.ijlmm.2025.03.004","url":null,"abstract":"<div><div>This study presents a comprehensive investigation of the structural performance of a small unmanned aerial vehicle (UAV) composite wing, integrating finite element analysis (FEA) and experimental whiffletree testing. The study focuses on a pusher-type UAV with a 2.9-m wingspan and 21 kg maximum takeoff weight. The distributed load from the Schrenk's method was converted into multiple point loads for whiffletree load by using the cantilever beam approach. When subjected to load within the limit load, the observed failure at the upper skin wing near the wing-body joint also complied with the failure results according to the finite element analysis with the Tsai-Wu failure index of 1. Conversely, the deflection comparison between the finite element analysis using whiffletree loads and the actual whiffletree testing showed good agreement, with maximum deflection values of 122 mm and 120 mm, respectively. With a difference of 1.67 % in maximum deflection, the study validates the wing's structural integrity up to the design limit load and identifies failure modes within this limit. This aircraft structure can withstand load factor up to 2.8, making it safe for standard maneuvers during flight operations.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 4","pages":"Pages 483-494"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D-Printed recycled polyethylene terephthalate (PET) sandwich structures – Influence of infill design and density on tensile, dynamic mechanical, and creep response","authors":"Ans Al Rashid,&nbsp;Muammer Koç","doi":"10.1016/j.ijlmm.2025.03.001","DOIUrl":"10.1016/j.ijlmm.2025.03.001","url":null,"abstract":"<div><div>Repurposing plastic waste is crucial to cope with the global population and rapid industrialization. Most plastic waste generated worldwide is mismanaged, leading to plastic pollution, landfill congestion, and microplastic contamination. Circular economy practices in the sustainable production and consumption of plastic are urgently needed to address these challenges, bringing plastics into closed-loop manufacturing and utilization. Additive manufacturing (AM) or 3D printing (3DP) have the potential to complement these efforts by facilitating on-demand, decentralized and flexible manufacturing using recycled plastics. In pursuit of circular materials for 3DP, this study investigates the influence of infill design and density on tensile and dynamic mechanical properties of 3D-printed recycled polyethylene terephthalate (rPET) sandwich structures. rPET filaments were produced using waste plastic bottles and were used for the 3DP process to produce sandwich structure coupons. In the first phase, the rPET filaments were tested for their mechanical properties revealing an average tensile strength of 111.99 MPa, failure strain of 1.20, and Young's modulus of 199.61 MPa, followed by the 3DP of tensile testing coupons with varying infill patterns (grid, tri-hexagon, octet, concentric, gyroid, and solid) and infill densities (25%, 50%, and 75%). The 3D-printed sandwich structures were evaluated for their dimensional stability and mechanical properties. All patterns demonstrated good dimensional stability, with minor variations from the CAD model. The mechanical properties of the concentric pattern at 50% infill (C50) stand out as the best among all infill types and patterns, exhibiting an average tensile strength of 34.65 MPa, failure strain of 0.067, Young's modulus of 464.32 MPa, and strength-to-weight ratio of 8.56 (S/W). In the final phase, the optimal infill pattern and density (i.e., C50) were also tested for their dynamic mechanical properties. The outcomes of this study will assist future research in developing robust 3D-printed parts using rPET, and the comprehensive approach presented in this study can be further adapted to develop novel recycled plastic waste-based composites for broader applications.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 4","pages":"Pages 442-452"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced performance of epoxy composites: A study on Walikukun-glass fiber hybrid reinforcement for automotive applications","authors":"Andoko Andoko ,&nbsp;Femiana Gapsari ,&nbsp;Afifah Harmayanti ,&nbsp;Abdul M. Sulaiman ,&nbsp;Riduwan Prasetya ,&nbsp;Nursyahbani Putri ,&nbsp;Mohammad Sukri Mustapa","doi":"10.1016/j.ijlmm.2025.03.003","DOIUrl":"10.1016/j.ijlmm.2025.03.003","url":null,"abstract":"<div><div>The increasing demand for sustainable, lightweight, and high-performance materials in the automotive industry necessitates innovative hybrid composite solutions. This study addresses the limitations of natural fibers like Walikukun (WF) in achieving high mechanical and thermal properties by hybridizing them with glass fibers (GF) in epoxy composites. Using a hot press technique, hybrid composites with varying WF and GF ratios were fabricated and evaluated for density, tensile strength, flexural properties, and thermal stability. The results revealed that the hybrid composite with 20 % WF and 10 % GF (W20G10) configuration achieved superior performance, with the highest flexural strength (96.11 ± 22.79 MPa), notable tensile strength (132.81 ± 30.73 MPa), and excellent thermal stability at 248.07 °C initial degradation temperature. Morphological analysis further confirmed improved fiber-matrix adhesion and effective stress distribution in W20G10 composites. This research contributes to the development of hybrid composites, offering valuable insights into optimizing material properties for advanced automotive applications.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 4","pages":"Pages 431-441"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation and optimization of machining parameters in Micro-WEDM of SMA to enhance performance","authors":"Rakesh R. Kolhapure ,&nbsp;Duradundi S. Badkar","doi":"10.1016/j.ijlmm.2025.03.002","DOIUrl":"10.1016/j.ijlmm.2025.03.002","url":null,"abstract":"<div><div>Ti–Ni Shape Memory Alloys (SMAs) are extensively used in biomedical applications due to their superior biocompatibility and mechanical properties over traditional biomaterial SS316L and Ti alloys. However, achieving high precision and surface integrity during machining remains a significant challenge. This study focuses on optimizing the Micro-Wire Electric Discharge Machining (μ-WEDM) parameters to enhance the machining efficiency and surface quality of Ti–Ni SMAs. An L27 orthogonal array (OA) and Grey Relational Analysis (GRA) were applied to optimize multiple machining responses, including Material Removal Rate (MRR), Surface Roughness (SR), Dimensional Deviation (DD), and Kerf Width (KW) by using Voltage (V), Capacitance (C), and Wire feed (WF) as process parameters. Analysis of Variance (ANOVA) was conducted to evaluate the contribution of each parameter. The results indicate that ‘C’ significantly influences MRR (78.40 %), DD (50.98 %), and KW (36.64 %), while ‘V’ has the highest impact on SR (57.62 %). The optimal parameter combination (105 V, 6 nF, 1 mm/min) improved machining efficiency by 2.79 % (GRG) increased from 0.6898 to 0.7091, minimized surface defects, and enhanced dimensional accuracy. Scanning Electron Microscope (SEM) analysis confirmed that optimized μ-WEDM parameters minimized surface defects, refined textures, and reduced micro-cracks, enhancing surface integrity also minimizing recast layer results in dimensional accuracy. Energy Dispersive Spectroscopy (EDS) analysis verified minimal contamination, ensuring biocompatibility, making μ-WEDM ideal for high-precision biomedical applications. Furthermore, the study emphasizes the environmental sustainability of μ-WEDM, highlighting its reduced material waste and lower energy consumption compared to traditional machining methods. By integrating robust statistical analysis and process control, the study offers new insights into achieving good surface quality and performance in medical field.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 4","pages":"Pages 537-550"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning in additive manufacturing: A comprehensive insight","authors":"Md Asif Equbal ,&nbsp;Azhar Equbal ,&nbsp;Zahid A. Khan ,&nbsp;Irfan Anjum Badruddin","doi":"10.1016/j.ijlmm.2024.10.002","DOIUrl":"10.1016/j.ijlmm.2024.10.002","url":null,"abstract":"<div><div>Additive manufacturing (AM) is a technological advancement gaining colossal popularity due to its advantages and simplified fabrication. AM facilitates the manufacturing of complex, light, and strong products from digitized designs. With recent advancements, AM can bring digital flexibility and improved efficiency to industrial operations. Despite the various advantages, there is continuous variation in the qualities of AM products, which remains the main challenge in the wide application of AM. The performance of printed parts is directly influenced by processing parameters, and adjusting the parameters in the AM process can be quite challenging. The barrier can be minimized by proper monitoring of the AM process and precise measurement of AM materials and components, which is difficult to achieve through analytical and numerical models. Current research demonstrates machine learning (ML) and its techniques as a novel way to reduce costs. It also helps achieve optimal process design and part quality using the fundamentals of the AM process. ML is a subcategory of artificial intelligence (AI) that enables systems to learn and improve from measured data and past experiences. The present paper is focused on presenting a broad understanding of the current applications of ML in AM and thus provides a solid background for practitioners and researchers to apply ML in AM. Very few earlier reviews were presented before, but their studies mostly focus on artificial neural network technology and other irrelevant papers. In addition, most papers were published in 2021 and 2022 and were not discussed in earlier reviews. This state-of-the-art review is based on the latest database collected from Web of Science (WoS), Publons, Scopus, and Google Scholar using machine learning and additive manufacturing as the keywords. Extensive information collected on the possible applications of ML in AM shows that ML can be effectively applied to improve AM part quality and process reliability.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 2","pages":"Pages 264-284"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental performance evaluation of a lightweight additively manufactured hydrodynamic thrust bearing","authors":"Collier Fais ,&nbsp;Isaiah Yasko ,&nbsp;Muhammad Ali ,&nbsp;Rick Walker ,&nbsp;Joe Walker","doi":"10.1016/j.ijlmm.2024.10.003","DOIUrl":"10.1016/j.ijlmm.2024.10.003","url":null,"abstract":"<div><div>In this paper, a lightweight additively manufactured (AM) fixed geometry hydrodynamic thrust bearing fabricated via laser powder bed fusion (LPBF) is experimentally compared to a traditionally manufactured cast aluminum alloy thrust bearing of similar design. The purpose of this study is to evaluate how weight-saving design features in the AM bearing affect active critical hydrodynamic performance parameters to better understand in-service viability. Under various static operating conditions, performance parameters such as hydrodynamic pressure distribution, minimum oil film thickness (MOFT), bearing temperature and increase in oil temperature are measured. Compared to the traditionally manufactured bearing, the AM bearing showed an average increase in minimum oil film thickness of 53 %, an average increase in trailing edge hydrodynamic pressure of 116 %, while exhibiting an average decrease in bearing temperature of 1 %. Experimental results are compared to numerical simulation showing reasonably good agreement.</div></div>","PeriodicalId":52306,"journal":{"name":"International Journal of Lightweight Materials and Manufacture","volume":"8 2","pages":"Pages 285-299"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}