Manufacturing Letters最新文献

{"title":"Hybrid CNN–Graph Transformer Encoder with Autoregressive Decoding for CAPP-GPT","authors":"Hassan Mohamed,&nbsp;Moayad AlNammi,&nbsp;Ahmed Azab","doi":"10.1016/j.mfglet.2026.07.025","DOIUrl":"10.1016/j.mfglet.2026.07.025","url":null,"abstract":"<div><div>Automating Computer-Aided Process Planning (CAPP) remains a significant challenge in digital manufacturing, particularly the direct generation of optimal Manufacturing sequences from Boundary Representation (B-Rep) CAD models or Constructive Solid Geometry (CSG) trees. Existing deep learning methods often fall short, either by sacrificing geometric and topological fidelity through conversion to intermediate representations or by focusing primarily on feature classification rather than full sequential plan generation. This paper presents a proof-of concept encoder–decoder Transformer architecture designed to address this gap. The encoder extracts local geometric information using convolutional neural networks (CNNs) applied to UV-grid samplings of CAD faces, enriched with explicit attributes of each face and edge (e.g., type and adjacency). In parallel, global relational context is captured by representing the CAD structure as a graph and processing it with a Graph Transformer. Through this joint representation, the encoder recognizes Manufacturing features, which are then passed to the Transformer-based decoder that autoregressively generates complete and optimal manufacturing plans token by token. We demonstrate the effectiveness of our approach on a challenging case study with interacting geometric features, where our model successfully produces logically consistent and optimal Manufacturing sequences; the focus of the case study, though, is on the decoder part of the architecture. The overall framework still provides a promising direction for CAPP directly from native CAD data.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 153-164"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096605","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":"Influence of heat treatment on the microstructure, hardness and sliding wear of PBF-LB AlSi10Mg across temperature variations","authors":"O. Olaogun,&nbsp;L. Semetse,&nbsp;P.A. Olubambi","doi":"10.1016/j.mfglet.2026.07.100","DOIUrl":"10.1016/j.mfglet.2026.07.100","url":null,"abstract":"<div><div>Metal additive manufacturing, particularly laser powder bed fusion (PBF-LB), has revolutionized the production of complex geometries with enhanced performance. This study investigates the effects of solution heat-treatment on the microstructure, hardness, and dry sliding wear behavior of PBF-LB manufactured AlSi10Mg alloy. Heat-treatments were performed at 450 °C, 500 °C, and 530 °C for varying durations, followed by water quenching, and designated as HT1, HT2, and HT3. Tribological behavior was assessed using a ball-on-disk tribometer, while microstructural and wear track features were characterized via scanning-electron-microscopy (SEM) and energy-dispersive-spectroscopy (EDS). Microhardness was measured using INNOVATEST-FALCON-507 tester. The as-built alloy exhibited fine cellular microstructure, which coarsened with increasing temperature and duration, leading to reduced silicon segregation and more uniform phase dispersion. An inverse relationship between microhardness and relative density was observed across heat treatment stages, governed by temperature dependent phase transformations and defect evolution. While HT1 and HT2 resulted in significant softening, HT3 showed an anomalous hardness recovery, likely due to precipitation hardening or microstructural stabilization. Relative density decreased at higher temperatures, attributed to pore coalescence and thermal degradation. Wear behavior was strongly influenced by debris generation and microstructural integrity. The as-built condition demonstrated superior wear resistance, characterized by high hardness, minimal volume loss, and stable coefficient of friction (CoF). In contrast, HT1 and HT2 exhibited degraded wear resistance due to increased debris and surface instability. HT3 showed partial recovery in wear performance, correlating with hardness stabilization and reduced defect density. These findings highlight the critical interplay between heat treatment, microstructure, and tribological response, emphasizing the need for optimized thermal post-processing to enhance the wear performance of PBF-LB AlSi10Mg alloys for demanding applications.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 858-867"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149095910","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":"A Multimodal Manufacturing Safety Chatbot: Knowledge Base Design, Benchmark Development, and Evaluation of Multiple RAG Approaches","authors":"Ryan Singh,&nbsp;Austin Hamilton,&nbsp;Amanda White,&nbsp;Michael Wise,&nbsp;Ibrahim Yousif,&nbsp;Arthur Carvalho,&nbsp;Zhe Shan,&nbsp;Reza Abrisham Baf,&nbsp;Mohammad Mayyas,&nbsp;Lora A. Cavuoto,&nbsp;Fadel M. Megahed","doi":"10.1016/j.mfglet.2026.07.012","DOIUrl":"10.1016/j.mfglet.2026.07.012","url":null,"abstract":"<div><div>Ensuring worker safety remains a critical challenge in modern manufacturing environments. Industry 5.0 reorients the prevailing manufacturing paradigm toward more human-centric operations. Using a design science research methodology, we identify three essential requirements for next-generation safety training systems: high accuracy, low latency, and low cost. We introduce a multimodal chatbot powered by large language models that meets these design requirements. The chatbot uses retrieval-augmented generation to ground its responses in curated regulatory and technical documentation. To evaluate our solution, we developed a domain-specific benchmark of expert-validated question and answer pairs for three representative machines: a Bridgeport manual mill, a Haas TL-1 CNC lathe, and a Universal Robots UR5e collaborative robot. We tested 24 RAG configurations using a full-factorial design and assessed them with automated evaluations of correctness, latency, and cost. Our top 2 configurations were then evaluated by ten industry experts and academic researchers. Our results show that retrieval strategy and model configuration have a significant impact on performance. The top configuration, selected for chatbot deployment, achieved an accuracy of 86.66%, an average cost of $0.005 per query, and an average end-to-end latency of 10.04 seconds. This latency is practical for delivering a complete safety instruction and is measured from query submission to full instruction delivery rather than generation onset. Overall, our work provides three contributions: an open-source, domain-grounded safety training chatbot; a validated benchmark for evaluating AI-assisted safety instruction; and a systematic methodology for designing and assessing AI-enabled instructional and immersive safety training systems for Industry 5.0 environments.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 30-41"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149095964","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":"Curriculum-Based Soft Actor-Critic for Multi-Section R2R Tension Control","authors":"Shihao Li,&nbsp;Jiachen Li,&nbsp;Christopher Martin,&nbsp;Zijun Chen,&nbsp;Dongmei Chen,&nbsp;Wei Li","doi":"10.1016/j.mfglet.2026.07.018","DOIUrl":"10.1016/j.mfglet.2026.07.018","url":null,"abstract":"<div><div>Precise tension control in roll-to-roll (R2R) manufacturing systems remains challenging under varying operating conditions and process uncertainties. While they perform well for systems with accurate models and small disturbances, model-based controllers face limitations when operating conditions deviate from design points or when process uncertainties are large. This paper presents a curriculum-based Soft Actor-Critic (SAC) approach as an alternative to R2R control under uncertainty. The method learns control policies through progressive exposure to various operating conditions without requiring system-specific parameter tuning. We train SAC with a three-phase curriculum with gradual expansion of reference trajectory diversity, from a narrow ranges (27N–33N) to full operational envelope (20N–40N), enabling the policy to generalize across the complete operational range. Evaluation on a three-section R2R system shows that the learned policy maintains tight tension tracking under nominal conditions and handles large disturbances (20N<span><math><mrow><mo>→</mo></mrow></math></span>40N step changes) well with a single control policy. The proposed approach offers an alternative to traditional model-based methods for applications where operating conditions vary and process uncertainties are significant, typical for R2R manufacturing of flexible devices.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 86-97"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149095965","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":"Design and Development of a Robust Open-Source Wire Arc Additive Manufacturing Platform","authors":"Vishnu Ramasamy,&nbsp;Michael Bastuga,&nbsp;Jacob G. Gregg,&nbsp;John J. Lewandowski","doi":"10.1016/j.mfglet.2026.07.020","DOIUrl":"10.1016/j.mfglet.2026.07.020","url":null,"abstract":"<div><div>Wire Arc Additive Manufacturing (WAAM) offers a cost-effective method for producing large-scale metallic components, yet its adoption remains constrained by the complexity and high capital cost of existing systems. This work presents a fully open-source, low-cost gantry-style WAAM platform called Arc One, engineered for mechanical rigidity, effective thermal isolation, scalability, and ease of customization. The system integrates commercially available welding hardware with modern open-source control architecture, enabling reliable large-scale deposition for research and training purposes. A 14 lb. thick-walled 316LSi stainless steel structure was fabricated to validate performance against comparable industrial WAAM builds. Real-time process data and subsequent material characterization confirmed stable deposition behavior with microstructural and hardness properties comparable to industrial systems for simple thick-walled geometries. All design files, firmware, and deposition data were released under open access to promote broader engagement in WAAM technology development.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 110-118"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149095967","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":"Data-Free Prediction of Aluminum Laser Welding Thermal Fields Using a Physics-Informed Neural Network","authors":"Zhengxiao Yu,&nbsp;Samuele Piandoro,&nbsp;Alessandro Fortunato,&nbsp;Teresa J. Rinker,&nbsp;Jennifer Bracey,&nbsp;Masoud M. Pour,&nbsp;Shun-Li Shang,&nbsp;Zi-Kui Liu,&nbsp;Jingjing Li","doi":"10.1016/j.mfglet.2026.07.038","DOIUrl":"10.1016/j.mfglet.2026.07.038","url":null,"abstract":"<div><div>Laser welding involves complex molten pool dynamics that critically affect weld quality and defect formation. In this work, a physics-informed neural network (PINN) model is developed to predict the temperature distribution and molten pool geometry in laser welding. The PINN incorporates the heat conduction equation into its loss function, ensuring that the predicted temperature field follows physical laws without relying on experimental training data. This model accepts spatial and temporal coordinates, material properties, and laser parameters as inputs and outputs the predicted temperature field. Validation against X-ray observations demonstrates that the predicted molten pool depth and width agree well with experiments, with relative error for penetration depth remained as low as 1.0%, while the deviation in the top weld width remained below 10%. The proposed PINN approach provides a fast, interpretable, and data-free framework for simulating temperature evolution and weld pool formation in laser welding.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 296-302"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096410","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":"oAssessment of Different Proximity Metrics in Clustering of Geometric Features: An Optimal Stock Design for Hybrid Manufacturing CAPP","authors":"E. Matengele,&nbsp;H. Osman,&nbsp;A. Azab,&nbsp;F. Baki","doi":"10.1016/j.mfglet.2026.07.034","DOIUrl":"10.1016/j.mfglet.2026.07.034","url":null,"abstract":"<div><div>Stock design is a critical yet underexplored factor in Hybrid Manufacturing (HM) process planning, directly influencing material efficiency and production cycle time. This study extends a constrained clustering framework for HM stock design by systematically evaluating how the choice of proximity metric influences cluster formation and downstream manufacturing performance. Four distance measures, Hausdorff, Modified Hausdorff, Chamfer, and Sum of Minimum Distances, were incorporated into a clustering optimization model and evaluated across multiple cluster counts on two industrially representative prismatic components: a 22-feature part (15 additive, 7 subtractive) and a 15-feature clip-holder (7 additive, 8 subtractive). Cluster similarity, quantified via the Adjusted Rand Index and validated through a blocked two-way ANOVA with replication across three aspect-ratio variants, revealed strong consensus at low cluster counts but increasing divergence as partitioning granularity grew, indicating that metric selection becomes critical at higher partitioning levels. The resulting configurations were integrated into an extended HM Computer-Aided Process Planning model to assess manufacturing cycle time. For the 22-feature part, the Sum of Minimum Distances achieved a 9.1% cycle time reduction relative to the Hausdorff baseline. For the clip-holder, multiple metrics converged to the same minimum cycle time, indicating that metric sensitivity is part-dependent. These findings provide a structured methodology for proximity metric selection in HM stock design and demonstrate its tangible impact on manufacturing efficiency.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 247-258"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096525","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":"Development of Novel Advanced Polymer and Composites Convergent System – Additively Reinforced Thermoforming (ART)","authors":"Ahmed Arabi Hassen,&nbsp;Madhura Limaye,&nbsp;Segun Isaac Talabi,&nbsp;Ryan Ogle,&nbsp;Tyler Smith,&nbsp;Vlastimil Kunc,&nbsp;Joshua Vaughan,&nbsp;Ryan Spencer,&nbsp;Nathaniel Root,&nbsp;Seokpum Kim,&nbsp;Vipin Kumar","doi":"10.1016/j.mfglet.2026.07.040","DOIUrl":"10.1016/j.mfglet.2026.07.040","url":null,"abstract":"<div><div>This paper presents the development of the patented Additively Reinforced Thermoforming (ART) system, a novel convergent manufacturing approach that integrates extrusion deposition Additive Manufacturing (AM) with thermoforming for producing structurally optimized composite parts with enhanced performance. Thermoforming, known for its economical fabrication of thermoplastic components, offers rapid cycle times and the capacity to utilize 3D-printed or other cost-effective mold systems. However, the process is also characterized by limitations such as restrictions on the complexity of mold geometries and variability in thickness, especially for deep drawn parts.<!--> <!-->The latter leads to regions of reduced strength, which become potential structural weak points. Consequently, support structures are often required to achieve geometrical stiffness and enhance the structural integrity of such products. To address these challenges, the ART process incorporates reinforcement components onto the polymer sheet, which remain fully retained during and after thermoforming, thereby enabling selective modification of material properties in the final product. The precision and reproducibility inherent in 3D printing renders it an ideal method for achieving the desired targeted reinforcement. Compared to standalone approach, the selected integrated gantry-based ART system can reduce the levelized cost per unit by approximately 5.6%, demonstrating the efficiency and cost benefits of the convergent system.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 311-320"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096531","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":"Effects of Roller Traverse Speed and Roller Rotational Speed on Powder Bed Density and Green Part Density in Binder Jetting Additive Manufacturing","authors":"Fahim Khan,&nbsp;Steven Kuntzendorf,&nbsp;Md Shakil Arman,&nbsp;Zhijian Pei","doi":"10.1016/j.mfglet.2026.07.095","DOIUrl":"10.1016/j.mfglet.2026.07.095","url":null,"abstract":"<div><div>This study investigates the effects of two process parameters (roller traverse speed and roller rotational speed) on powder bed density and green part density in binder jetting additive manufacturing using silicon carbide powder at higher powder dispense amount (longer dispense delay and slower recoat speed). Roller traverse speed and roller rotational speed are used to spread the powder across the powder bed and affect powder packing. Experiments were conducted at three levels of roller traverse speed (5, 10, and 15 mm/ s) and three levels of roller rotational speed (100, 200, and 300 rpm). As roller traverse speed increased from 5 to 15 mm/s, powder bed density decreased from 2.12 to 1.70 g/cm³, while the corresponding average green part density decreased from 1.72 to 1.58 g/cm³. Increasing roller rotational speed from 100 to 300 rpm reduced powder bed density from 2.13 to 1.69 g/cm³ and green part density from 1.72 to 1.59 g/cm³. The one-way ANOVA results reveal that both roller traverse speed and roller rotational speed have statistically significant effects on powder bed density and green part density. These findings provide useful insights into optimizing binder jetting additive manufacturing process parameters to achieve desired green part density without employing powder bed compaction.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 813-820"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096609","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":"Investigating the Effects of Iron Oxide Nano Fillers on the Structural, Viscoelastic, and Mechanical Behavior of Additively Manufactured PDMS-Fe2O3 Composites","authors":"Sumaiya Rahman,&nbsp;Muhammad P. Jahan","doi":"10.1016/j.mfglet.2026.07.103","DOIUrl":"10.1016/j.mfglet.2026.07.103","url":null,"abstract":"<div><div>Magnetoactive polymer composites have gained significant scientific attention due to their rapid and reversible reaction to external magnetic fields. This study aims to investigate the feasibility of developing Iron oxide (<span><math><mrow><msub><mrow><mi>F</mi><mi>e</mi></mrow><mn>2</mn></msub><msub><mi>O</mi><mn>3</mn></msub></mrow></math></span>) embedded Polydimethylsiloxane (PDMS) composites with direct ink writing (DIW) based 3D printing technique. PDMS-Fe₂O₃ composites with three different weight ratios of Iron oxide powder (i.e., 1%, 5%, and 10%) were formulated for 3D printing using DIW technique. The effects of varied concentrations of Iron oxide particles on the structural, mechanical, and rheological properties have been investigated. The printing parameters were kept constant with only varying printing pressure for uninterrupted printing of samples. The printing was done with a narrow-gauge needle of nozzle diameter 410 μm. The average particle size of Iron oxide was 30 nm in size, which was chosen to maintain the viscosity of the printing material low enough to avoid clogging at the nozzle. The samples structures were characterized by X-ray diffraction (XRD) analysis, rheological analysis, and tensile testing. The XRD patterns verified that <span><math><mrow><msub><mrow><mi>F</mi><mi>e</mi></mrow><mn>2</mn></msub><msub><mi>O</mi><mn>3</mn></msub><mspace></mspace></mrow></math></span> did not form a new crystalline phase and that the PDMS stayed amorphous. Without changing the PDMS structure, the filler remained distributed within the PDMS-Fe₂O₃ composites. Tensile data showed that higher stress values (2.8–3.04 MPa) were observed in the 1 wt% and 5 wt% Fe₂O₃–PDMS composites at the same strain as pure PDMS, suggesting enhanced strength and stiffness of printed Fe₂O₃–PDMS composites with good flexibility. The 10% Fe₂O₃-PDMS composite, on the other hand, exhibited brittle and less elastic behavior and fractured quicker at lower stresses (1–2 MPa). Although pure PDMS is the most elastic among all the samples, it exhibited somewhat reduced stress range compared to 1 wt% and 5 wt% Fe₂O₃–PDMS composites. To ensure smooth extrusion and good shape retention during 3D printing, this study also performed rheological tests to assess the viscoelastic behavior and flowability of PDMS with Fe₂O₃ inks. The stiffness and energy dissipation of PDMS were increased due to increase in both storage and loss moduli with Fe₂O₃ nanofillers.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 885-894"},"PeriodicalIF":2.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149095913","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}