CIRP Journal of Manufacturing Science and Technology最新文献

{"title":"Suppression of chatter in thin-walled component milling through shear thickening fluids","authors":"Shuqi Wang ,&nbsp;Shengjie Zhou ,&nbsp;Dongliang Gao ,&nbsp;Xiaoqiu Xu ,&nbsp;Chunlei He","doi":"10.1016/j.cirpj.2026.01.007","DOIUrl":"10.1016/j.cirpj.2026.01.007","url":null,"abstract":"<div><div>In the milling of thin-walled components, the inherently low stiffness of these structures makes the occurrence of chatter a critical issue that significantly limits machining accuracy and productivity. To address this challenge, this study proposes a novel approach for chatter suppression based on the shear thickening effect. Two representative types of shear thickening fluids (STFs)—silicon dioxide-polyethylene glycol (SiO₂-PEG) and cornstarch-water—are experimentally investigated. Initially, the modal parameters of thin-walled workpieces, both with and without the application of STFs, are determined separately through experimental modal analysis. Subsequently, a nonlinear milling dynamics model is formulated using Hamilton’s principle, incorporating the kinetic energy, strain energy, boundary potential energy, and strain potential energy of the system, as well as the rheological and mechanical properties of the STF. The stability lobe diagram is then computed using the full-discretization method to analyze the dynamic stability of the system. To further validate the vibration suppression effectiveness of the STFs, milling vibration tests are conducted using different types and mass fractions of the fluid. The results indicate that the application of STF significantly reduces the natural frequency and increases the damping ratio of the cutting system, thereby achieving a notable suppression of milling vibrations and improving the milling surface roughness.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 238-260"},"PeriodicalIF":5.4,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface quality classification in burnished aluminum alloys based on nonlinear dynamic characteristics and machine learning","authors":"Cong Ding ,&nbsp;Shiqing Feng ,&nbsp;Xing Liu ,&nbsp;Michael G. Bryant ,&nbsp;Yan Zhao ,&nbsp;Jianfei Han ,&nbsp;Zhongyu Piao","doi":"10.1016/j.cirpj.2026.01.005","DOIUrl":"10.1016/j.cirpj.2026.01.005","url":null,"abstract":"<div><div>Active control of surface quality requires insight into both processing parameters and the nonlinear dynamic behavior of machining systems. However, existing studies mainly focus on microscopic surface attributes and often overlook their relationship with system-level nonlinear dynamics, limiting both predictive accuracy and mechanistic understanding. To address this gap, this study investigates the surface burnishing process (SBP) by integrating process parameters, vibration-based nonlinear dynamic analysis, and machine learning. A quantitative intrinsic mode function (IMF) screening method based on ensemble empirical mode decomposition (EEMD) and power spectral density (PSD) was proposed to enhance vibration signal denoising and feature reliability. Chaotic behavior of the SBP system was confirmed by a positive maximum Lyapunov exponent (λ_max&gt;0), and a set of recurrence quantification analysis (RQA) parameters was extracted. Three feature scenarios, SBP parameters with positional encoding, chaotic features, and their combination, were evaluated for classifying surface roughness and hardness. Results showed that surface roughness was predominantly governed by burnishing parameters, whereas hardness prediction benefited more from RQA parameters reflecting the surface deformation stability. The findings clarify the distinct roles of deterministic and dynamic factors in surface-quality formation and provide a flexible, physically interpretable framework for data-driven surface-quality prediction and adaptive manufacturing applications.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 223-237"},"PeriodicalIF":5.4,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodology to quantify the recyclability of design alternatives for highly integrated technical products applied to lithium-ion batteries","authors":"S. Hansen ,&nbsp;F. Vysoudil ,&nbsp;A. Dlugosch ,&nbsp;J. Riech ,&nbsp;M. Mennenga ,&nbsp;S. Blömeke ,&nbsp;T. Vietor ,&nbsp;C. Herrmann","doi":"10.1016/j.cirpj.2026.01.003","DOIUrl":"10.1016/j.cirpj.2026.01.003","url":null,"abstract":"<div><div>High manufacturability and usability demands lead to an increasing complexity of technical products, which in turn reduces their recyclability. However, to assess the consequences a dedicated Design for Recycling would have in End of Life is challenging due to the complex dependencies between product design and recycling systems which are particularly difficult to estimate in early-stage product development. It is especially the economic benefits, however, that need to be made transparent to reach a widespread application of Design for Recycling in industry. Therefore, this paper presents a methodology to assess economic aspects of a design-dependent End of Life behavior of a product with minimal initial information, which reflects the constraints typical for Product Development Processes. It offers a structured analytical evaluation of design impacts on costs and revenues as well as on the recycling progress that the single steps throughout the End-of-Life process chain would entail. The methodology is exemplarily applied to two electric bike batteries that exhibit significant differences in terms of their recyclability. Results show that these differences are clearly identifiable and the advantages of the more recycling-friendly design can be demonstrated.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 207-222"},"PeriodicalIF":5.4,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-based feature enhancement method and physics constraint Transformer model for multi-step tool wear and RUL prediction","authors":"Shanglei Jiang ,&nbsp;Haoyuan Zhang ,&nbsp;Zhengmao Chen ,&nbsp;Yuwen Sun ,&nbsp;Xuexia Liu","doi":"10.1016/j.cirpj.2026.01.001","DOIUrl":"10.1016/j.cirpj.2026.01.001","url":null,"abstract":"<div><div>In intelligent manufacturing, tool wear monitoring (TWM) and remaining useful life (RUL) prediction are crucial for improving production quality and efficiency. However, achieving accurate and reliable multi-step (long-term) predictions remains a substantial challenge. This research proposes a feature enhancement method and constructs a Transformer model that embeds hard-soft physics constraints for multi-step wear and RUL prediction. Firstly, the fast adaptive Brownian bridge aggregation algorithm (fABBA) is employed to extract the features from multiscale signals during machining, alleviating the reliance on domain knowledge inherent in traditional feature engineering to some extent. On this basis, a physics-based feature enhancement method is proposed to improve the model’s generalization. Secondly, a Transformer model based on multi-head self-attention and cross-attention mechanisms is constructed for multi-step tool wear and RUL prediction. Meanwhile, a hard-soft physical constraint embedding module is designed to ensure that the model's output has a certain degree of physical interpretability. Finally, the PHM2010 and self-constructed datasets are employed to verify the effectiveness of the proposed method. Shapley Additive Explanations (SHAP) analysis method is used to quantitatively analyze the contribution of features to the model. The wear comparison experiments on the PHM2010 dataset, using C6 as the test set, show that the proposed model achieves RMSE values of 1.680672 <span><math><mo>±</mo></math></span> 0.137001, 2.220760 <span><math><mo>±</mo></math></span> 0.145516, 3.430798 <span><math><mo>±</mo></math></span> 0.485509, and 5.106184 <span><math><mo>±</mo></math></span> 0.690110 for 12, 24, 36 and 48 step predictions, respectively. Even for the ultra-long wear prediction, the R² remains at 0.981760 <span><math><mo>±</mo></math></span> 0.005227, which is better than GRU, BiGRU, BiGRU-AT, and TCN models.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 179-206"},"PeriodicalIF":5.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature field characteristics in rotary longitudinal-torsional ultrasonic machining of unidirectional carbon fiber reinforced polymer (CFRP)","authors":"Ziqiang Zhang ,&nbsp;Feng Jiao ,&nbsp;Yuanxiao Li","doi":"10.1016/j.cirpj.2025.12.019","DOIUrl":"10.1016/j.cirpj.2025.12.019","url":null,"abstract":"<div><div>Enhancing the drilling quality of carbon fiber reinforced polymer (CFRP) holds significant importance for advancing its application in aerospace and other fields. The temperature during CFRP core drilling critically impacts hole quality, and incorporating ultrasonic vibration during machining can effectively reduce this temperature. Predicting workpiece temperature is vital for selecting process parameters and enhancing hole quality of CFRP core drilling. However, current research on rotary ultrasonic machining (RUM) of CFRP predominantly focuses on experimental investigations, which relatively few reports on the temperature prediction. Utilizing the benefits of longitudinal-torsional ultrasonic vibration to reduce temperature, this paper establishes a temperature prediction model for rotary longitudinal-torsional ultrasonic machining (RLTUM) of unidirectional CFRP and analyzes the temperature field characteristics. Initially, the heat source properties in the machining process are analyzed, followed by an examination of heat transfer characteristics using the heat source method. Furthermore, the surface morphology of CFRP hole wall under different machining conditions was compared. Experimental verification confirms the model’s accuracy, demonstrating its capability to predict temperature evolution and variation trends with process parameters. The peak temperature prediction errors perpendicular and parallel to the fiber direction are 7.09–12.63 % and 5.54–14.36 %, respectively. Implementing longitudinal-torsional ultrasonic vibration reduces temperature during the core drilling process. As the fiber orientation angle increases, the corresponding peak temperature decreases, and the peak temperatures for different fiber orientation angles are symmetrical. This model serves as a valuable reference for selecting process parameters to improve CFRP drilling quality.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 145-162"},"PeriodicalIF":5.4,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A grey-box approach based on Johnson-Cook constitutive model to improve predictions of mechanical loads of cutting simulations for normalized AISI 1045","authors":"Jan Wolf ,&nbsp;Erik Krumme ,&nbsp;Nithin Kumar Bandaru ,&nbsp;Martin Dienwiebel ,&nbsp;Andreas Zabel ,&nbsp;Hans-Christian Möhring","doi":"10.1016/j.cirpj.2025.12.014","DOIUrl":"10.1016/j.cirpj.2025.12.014","url":null,"abstract":"<div><div>In machining, high temperatures and strain rates impact the flow stress of the workpiece material, making it essential to understand the materials behaviour in these process conditions for meaningful finite element analysis (FEA) of the cutting process. The Johnson-Cook constitutive model, despite being the most widely applied, is reported to struggle in capturing the material behaviour outside of the reference conditions it was calibrated on. However determining these parameters in conventional material tests is challenging. To solve this issue, this study proposes a grey-box approach which aims to increase the accuracy of process force prediction of FEA, employing a Johnson-Cook model determined by experiments conducted on a Split-Hopkins Pressure Bar and compression tests at elevated temperatures on a Gleeble 3800c for AISI 1045, over a variety of cutting parameters. In total 110 cutting experiments and their corresponding simulations were carried out in a fully factorial experimental design with eleven cutting speeds and ten uncut chip thicknesses. Succeeding the white-box model, a black box model is trained to capture the non-linear behaviour between the simulation and the cutting experiments. Among the tested algorithms, XGBoost and Support Vector Regression outperformed Random Forests and Neural Network for predicting cutting force and feed force. The proposed grey-box approach showed an improved capability of predicting cutting force and feed force, reducing the mean absolute error and mean squared error compared to the white-box model by 97.9 % and 99.9 % for cutting force and by 94.9 % and 99.7 % for feed force, respectively. The grey-box model achieved a mean error of 1.3 % with a standard deviation of 0.1 in process force prediction.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 163-178"},"PeriodicalIF":5.4,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finite element modeling of indexable insert drilling processes in stainless steel","authors":"Mikel Etxebeste ,&nbsp;Gorka Ortiz-de-Zarate ,&nbsp;Homar Lopez-Hawa ,&nbsp;Pedro J. Arrazola","doi":"10.1016/j.cirpj.2025.12.018","DOIUrl":"10.1016/j.cirpj.2025.12.018","url":null,"abstract":"<div><div>Indexable insert drills play a crucial role in high-performance drilling, particularly for large-diameter holes and difficult-to-machine materials. Although FEM is a powerful tool for analyzing and optimizing drilling processes, limited research has focused on indexable insert drills, and the efficient simulation of large-diameter drills with complex cutting geometries remains a significant challenge. This study presents an optimized and computationally efficient FEM model for indexable insert drills, developed in AdvantEdge™ 3D, capable of predicting thermomechanical loads (thrust force, torque, stress, temperature) and chip morphology during drilling and redrilling of AISI 316 L stainless steel. The key innovation lies in a computational approach that significantly reduces simulation time while maintaining high predictive accuracy. The model incorporates a novel tool–workpiece configuration with a slotted workpiece that enables the drill to reach nominal feed rate immediately upon engagement, accelerating convergence toward thermomechanical steady-state. Model optimization was achieved through a systematic evaluation of the most influential input parameters, surpassing the capabilities of previous FEM approaches and providing new validated insight into drilling process modeling. A comprehensive sensitivity analysis of Johnson–Cook flow stress parameters, friction coefficients, and mesh size was performed to optimize both accuracy and computational efficiency. The model was experimentally validated through complete-drill tests (both inserts mounted) and novel single-insert tests (one insert mounted) across a wide range of cutting conditions, including redrilling with varying pilot hole diameters. The optimized model accurately predicts torque, thrust forces, and chip morphology (average error: 16 %), while providing detailed stress and temperature distributions. Thrust force underprediction remains the primary limitation, identified as originating from the central insert, where Build-Up Edge (BUE) formation was observed at low cutting speeds near the drill tip.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 130-144"},"PeriodicalIF":5.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent interpretable milling force prediction: A method based on vibration signals fusing data-driven and physical features","authors":"Wen Hou ,&nbsp;Tong Zhu ,&nbsp;Jiachang Wang ,&nbsp;Song Zhang","doi":"10.1016/j.cirpj.2025.12.020","DOIUrl":"10.1016/j.cirpj.2025.12.020","url":null,"abstract":"<div><div>To address the limitations in accuracy and interpretability of milling force prediction, this research proposes HyDCFF-Net, an interpretable model integrating physical time-frequency features with deep learning. First, vibration signals are processed using sliding windows, and a dual-channel neural network is developed to fuse these features, establishing a robust nonlinear mapping to milling force. Next, the Captum framework provides interpretability by visualizing feature contributions. Finally, extensive experiments under varied conditions validate its high prediction accuracy and strong generalization, achieving R² scores above 0.98 on primary tests with robust cross-dataset performance, demonstrating its effectiveness as a reliable milling force monitoring solution.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 95-129"},"PeriodicalIF":5.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive analysis of friction stir deposited Inconel 600: Thermal, structural, and mechanical insights","authors":"Neeraj K. Mishra,&nbsp;Jignesh Nakrani,&nbsp;Amber Shrivastava","doi":"10.1016/j.cirpj.2025.12.016","DOIUrl":"10.1016/j.cirpj.2025.12.016","url":null,"abstract":"<div><div>Solid-state deposition is one of the promising research areas. Here, the material does not melt during the process; instead, the material is plasticized to achieve the desired deformation. The present work employs friction stir metal deposition to create a three-dimensional wall structure of Inconel 600 through layer-by-layer deposition. A defect-free, fully consolidated wall without visible voids was deposited with more than 100 layers. The deposition was performed at 2500 RPM, with a 5 mm/min plunge feed and a 220 mm/min forward feed rate. The layer thickness was found to be decreasing in the build direction. Thermal history showed that layer deposition reheats the substrate and previously deposited layers. The temperature in the deposited wall varies in deposition and build direction. The heating-cooling cycle at the ends of the deposit is very different from the remaining portion of the deposit. Ends are exposed to higher temperatures for relatively shorter periods and once in every layer of deposition. In contrast, the remaining deposition is exposed to higher temperatures for a more extended period and twice in one layer of deposition. Also, a tool with an associated flash (Tool-flash) affects the heating of layers. The tool-flash's leading edge has a lower temperature than the tool flash's trailing edge, and the material beneath the tool-flash is heated cyclically. Microstructural investigation explained the effect of this non-uniformity of temperature in the grain morphology. Electron back scattered diffraction (EBSD) showed dynamic recrystallization-driven grain refinement where the grain size of the base material was 16–22μm, which changed to a final grain size of 8.3μm after FSMD. Further investigation along the build direction showed a trend of increasing grain size from the bottom towards the top with some alternate bands of fine grain region near the interface. Grains near the interface were as small as 0.1 μm. Electron backscattered diffraction (EBSD) results also showed that most of the grains were equiaxed with the presence of twin boundaries. Microhardness measurement showed decreasing trend along build direction, which is inline with the grain morphology and Hall Petch’s relationship. The tensile strength of deposition in the longitudinal direction showed comparable mechanical properties with the base material with a deposition efficiency of 78.3 %. Fractography of the failed samples showed ductile fracture with the significant presence of dimples and some parabolic dimples due to some delamination of layers. Energy dispersive spectroscopy results showed no elemental segregation, which was confirmed with uniform element distribution on the fracture surface.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 81-94"},"PeriodicalIF":5.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of composition, recycling and processing on deep drawing performance of automotive 6016 aluminium sheets","authors":"Angela Thum ,&nbsp;Emir Hodžić ,&nbsp;Josef Domitner ,&nbsp;Stefan Pogatscher","doi":"10.1016/j.cirpj.2025.12.012","DOIUrl":"10.1016/j.cirpj.2025.12.012","url":null,"abstract":"<div><div>The ability to undergo complex forming operations without failure due to necking or cracking is an essential feature for automotive sheet material. The present study examines the effects of chemical composition and processing parameters on the mechanical properties of industrial 6016 aluminium sheets. The homogenization process and the Mn content were used to investigate the influence of dispersoids. Moreover, the solution treatment, the Si content and the use of recycling-friendly compositions were studied. A (semi-)industrial scale deep drawing tool with a fixed drawing depth and varying blank holder force was used to characterize the formability of the sheets at different drawing speeds. An analysis of the strain hardening behaviour via Kocks-Mecking plots revealed remarkable predictive power, enabling the estimation of the behaviour under complex sheet forming conditions from tensile testing. Microstructural investigations demonstrated that dispersoids or constituent particles exerted a minimal influence on strain hardening at high degrees of deformation, whereas dissolved Si exerted a significant influence, resulting in markedly enhanced forming performance. This is linked to the suppression of dynamic recovery, which in turn leads to the interesting results that an alloy produced with higher recycled content performed very well.</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"65 ","pages":"Pages 70-80"},"PeriodicalIF":5.4,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}