Journal of Manufacturing Systems最新文献

{"title":"Online sequential decision making of multi-stage assembly process parameters based on deep reinforcement learning and its application in diesel engine production","authors":"Yi-Tian Song ,&nbsp;Yan-Ning Sun ,&nbsp;Li-Lan Liu ,&nbsp;Jie Wu ,&nbsp;Zeng-Gui Gao ,&nbsp;Wei Qin","doi":"10.1016/j.jmsy.2025.08.012","DOIUrl":"10.1016/j.jmsy.2025.08.012","url":null,"abstract":"<div><div>Maintaining fixed parameters during batch assembly of complex mechanical products often results in quality inconsistencies due to time-varying operational conditions, including equipment performance degradation, production environment disturbance, and operator skill variations. This operational reality necessitates online parameter adaptation mechanisms to counteract progressive quality deviations. While complex assemblies inherently involve sequential multi-stage workflows across distributed stations, conventional optimization strategies often employ monolithic parameter adjustments that neglect error propagation effects and inter-stage quality interdependencies. To address the dual challenges of dynamic operating conditions and multi-stage coordination, this study proposes an online sequential decision-making framework based on deep reinforcement learning. First, a causal inference model for assembly quality prognosis is constructed by integrating the greedy equivalence search algorithm with domain-specific expert knowledge, enabling systematic modeling of multi-stage quality dependencies. Subsequently, the multi-stage parameters optimization problem is formalized as a Markov decision process, with innovatively defined state space as assembly progress, action space as adjusted parameters range, and physics-informed reward function derived from quality inference results. Building on this, the proximal policy optimization algorithm is improved by stage-aware experience replay and gradient alignment constraints to learn the optimal policy, and then select the optimal action. Experiments on a real-world diesel engine assembly dataset demonstrate a 17.16 % improvement in product qualification probability, significantly outperforming conventional methods. The proposed framework effectively captures time-varying assembly characteristics and achieves cross-stage parameter coordination through sequential decision-making, offering a novel data-driven solution for quality control in complex product assembly systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 1252-1268"},"PeriodicalIF":14.2,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932313","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":"Step-time measurement: A scalable sub-cycle time defining methodology for anomaly detection and predictive maintenance in sequential production lines","authors":"Jon Zubieta ,&nbsp;Unai Izagirre ,&nbsp;Luka Eciolaza ,&nbsp;Asier Saez de Buruaga ,&nbsp;Lander Galdos","doi":"10.1016/j.jmsy.2025.08.011","DOIUrl":"10.1016/j.jmsy.2025.08.011","url":null,"abstract":"<div><div>Sub-cycle time periods from machines in production lines offer valuable insights into component-level health. They enable data-driven condition monitoring without the need for additional sensors. However, the lack of a standardized methodology for defining these sub-cycle time periods limits the practicality and scalability of this approach in real-world applications. We propose a scalable methodology to define sub-cycle time periods within the machine cycle time, using Programmable Logic Controllers (PLCs) programmed in compliance with the IEC 60848 standard. To achieve scalability, the proposed methodology makes sub-cycle time period definition automatic, simple and thus, fast. This is achieved by defining each sub-cycle time period as the total activation time of a Step. For this reason, the sub-cycle time periods defined with this methodology are named “Step-time”s. Because the methodology does not depend on the type of action or actuator involved, and because it can be applied to any step without requiring changes to the overall program structure, it can be easily replicated across multiple steps, modules, or even machines. This modularity enables a scalable deployment of Step-time measurements, whether for a few components or across entire production lines. Moreover, our methodology offers deeper insights into machine behavior by distinguishing between different operational contexts for the same component. To assess its feasibility in industrial production environments, we developed two implementation approaches, one based on Structured Text (ST) and another using Sequential Function Charts (SFC). The results demonstrate that machine anomalies such as air leaks, pressure drops and fluctuations in pneumatic circuits, are accurately reflected in Step-times. This confirms the high resolution of the Step-times and highlights its potential for powering data-driven condition monitoring systems in future works. Finally, the data acquisition results indicate that the proposed methodology has minimal impact on the PLC scan-cycle, making it suitable for most industrial use cases.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 1-11"},"PeriodicalIF":14.2,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934093","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":"GRU-based real-time scheduling method for production-logistics collaboration in digital twin workshop","authors":"Wenchao Yang ,&nbsp;Boxuan Zhang ,&nbsp;Guofu Luo ,&nbsp;Linli Li ,&nbsp;Xiaoyu Wen ,&nbsp;Hao Li ,&nbsp;Haoqi Wang","doi":"10.1016/j.jmsy.2025.08.013","DOIUrl":"10.1016/j.jmsy.2025.08.013","url":null,"abstract":"<div><div>In modern workshops with high customization requirements, production is typically conducted under a small-batch, multi-variety order mode. Under such conditions, random order arrivals and fuzzy manufacturing times, caused by fluctuations in workshop conditions, present significant challenges to real-time scheduling and control. To address these issues, this study proposes a real-time scheduling method for production-logistics collaboration (RT-SMPLC) based on gated recurrent units (GRUs) in a digital twin (DT) workshop. Firstly, a comprehensive RT-SMPLC framework was constructed. Leveraging virtual-physical interaction, a dynamic mapping environment is established to capture the real-time status information of production elements. Secondly, the scheduling process is guided by a task priority index that facilitates the selection of the optimal production-logistics resource group for each task. This priority index is iteratively optimized through virtual evolution and GRU-based prediction. Finally, the operation assignment result is fed back to the physical workshop for execution in real time via industrial communication protocols and networks, enabling closed-loop control through virtual-to-physical interaction. The proposed method was validated on a DT-based experimental platform using real production cases. Comparative experiments across three different-scale scenarios and three algorithms demonstrate that RT-SMPLC effectively reduces makespan, energy consumption, and tardiness, while exhibiting robust real-time responsiveness.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 1269-1289"},"PeriodicalIF":14.2,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932245","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":"Grating interferometer: The dominant positioning strategy in atomic and close-to-atomic scale manufacturing","authors":"Can Cui ,&nbsp;Xinghui Li ,&nbsp;Xiaohao Wang","doi":"10.1016/j.jmsy.2025.08.008","DOIUrl":"10.1016/j.jmsy.2025.08.008","url":null,"abstract":"<div><div>The rapid evolution of manufacturing technologies has entered its third phase, with a focus on atomic and close-to-atomic scale manufacturing (ACSM), a pivotal advancement driving progress in precision fabrication. One of the cores of ACSM is ultra-precise positioning technologies, which are critical for achieving the required precision and efficiency in nanoscale manufacturing. Grating interferometer has emerged as the leading strategy due to its accuracy, scalability, and stability. Recent advancements in this technology have further solidified its role as a dominant solution in ultra-precision metrology. This review provides an overview of grating interferometer as a positioning tool for ACSM, starting with its application domains and advantages, followed by a detailed explanation of its fundamental principles. We then present a comprehensive comparison of different representative grating interferometers. Additionally, we perform quantitative multi-source measurement error analysis, discuss methods for compensating errors, and explore various phase measurement techniques. Finally, we conclude with a forward-looking perspective on the future development of grating interferometer, highlighting emerging trends and potential breakthroughs.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 1227-1251"},"PeriodicalIF":14.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913028","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":"Large language model-enabled cognitive agent for self-aware manufacturing","authors":"Shanhe Lou,&nbsp;Runjia Tan,&nbsp;Yanxin Zhou,&nbsp;Ziyue Zhao,&nbsp;Yiran Zhang,&nbsp;Chen Lv","doi":"10.1016/j.jmsy.2025.08.015","DOIUrl":"10.1016/j.jmsy.2025.08.015","url":null,"abstract":"<div><div>Although industrial automation has advanced significantly at the level of manufacturing units and production lines, system-level automation remains constrained by the limited cognitive abilities of current manufacturing systems. To address this challenge, this work proposes a cognitive agent (CA) that leverages a large language model (LLM) as its core to facilitate self-aware manufacturing. The cognitive capabilities of CA are facilitated through the combination of retrieval-augmented generation (RAG) and in-context learning. RAG allows CA to retrieve relevant subgraphs from an industrial knowledge graph (IKG) after interpreting natural language commands, thereby establishing focused context awareness and autonomously generating executable manufacturing instructions. Meanwhile, in-context learning enables CA to adapt to specific requirements based on contextual examples without retraining. These techniques empower CA with domain-specific cognition, fostering self-awareness in a flexible and cost-effective manner. Two case studies on pick-and-place and disassembly validate CA's effectiveness in task planning within a lab-scale manufacturing unit. The results demonstrate that the proposed approach surpasses traditional LLM-based methods in task executability and goal achievement, offering a novel perspective on advancing manufacturing systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 1213-1226"},"PeriodicalIF":14.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913026","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":"Tool wear prediction in milling process using physics-informed machine learning and thermo-mechanical force model with monitoring applications","authors":"Farzad Pashmforoush ,&nbsp;Arash Ebrahimi Araghizad ,&nbsp;Erhan Budak","doi":"10.1016/j.jmsy.2025.08.014","DOIUrl":"10.1016/j.jmsy.2025.08.014","url":null,"abstract":"<div><div>Accurate wear estimation of milling tools is critical for enhancing the productivity and reliability of machining processes, ensuring consistent product quality while minimizing unexpected tool failure, downtime and machining costs. Traditional approaches, often based on pure experimental and data-driven machine learning (ML) methods, demand extensive, costly wear testing to gather the necessary datasets, which limits their utility in practical industrial monitoring. To address this gap, this work presents a novel physics-informed machine learning (PIML) approach of wear estimation by integrating analytical models with ML techniques. The PIML model utilizes a wear-inclusive thermo-mechanical model to estimating cutting forces considering flank wear and edge forces, with special focus on its adaptation to milling operations and addressing the complexities of milling dynamics. The methodology is demonstrated on Steel 1050, a widely used medium-carbon steel alloy in industrial machining applications. As shown by the results, this hybrid model shows high predictive accuracy, achieving R² values exceeding 98 % for force prediction and 95 % for tool wear estimation, with corresponding RMSE values below 14 N and 8 µm, respectively. Notably, the use of the PIML framework improved tool wear prediction accuracy by over 16 % compared to using ML alone. Another important finding is the significant role of edge forces under severe wear conditions, with their contribution to average cutting forces increasing from 40 % to 57 % at low feed rates, and from 27 % to 45 % at higher feed rates. Using this enhanced model, a simulation-based dataset was generated to train an inverse ML model for estimating tool wear considering milling forces and cutting parameters. The inverse ML model exhibited robust predictive performance, offering a practical and accurate solution for tool wear estimation. This study emphasizes the promising potential of integrating thermo-mechanical model with ML algorithms in machining applications, establishing a foundation of tool wear condition monitoring through milling force data. The presented approach can contribute to enhanced process control, optimized tool usage, and reduced operational costs. Furthermore, it supports the transition to Industry 4.0 by enabling automation and unsupervised manufacturing, where real-time tool wear monitoring and adaptive control can be achieved with minimal human intervention, driving more intelligent and efficient manufacturing systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 1192-1212"},"PeriodicalIF":14.2,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913027","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":"Time series prediction for lock nuts production quality driven by information fusion and data-model hybrid","authors":"Tianzi Tian ,&nbsp;Dunwang Qin ,&nbsp;Ning Wang ,&nbsp;Jun Yang ,&nbsp;Kai Wu","doi":"10.1016/j.jmsy.2025.08.005","DOIUrl":"10.1016/j.jmsy.2025.08.005","url":null,"abstract":"<div><div>The locking performance of nuts directly impacts the lifespan and reliability of assembled products. However, certain aerospace nuts undergo over 120 h of rigorous testing per batch, causing increased costs and delayed product delivery. Therefore, accurately predicting production quality and remaining testing time (RTT) is crucial for improving efficiency. Facing this new challenge, this paper proposes a data-model hybrid time series prediction method based on quality information fusion. First, considering that the monitoring data contains two sets of related features, we introduce a multi-task parallel deep learning (MTL) network with a temporal self-attention mechanism (TSAM). The TSAM assigns importance to key degradation information, while MTL leverages shared feature information to capture more accurate long-term trends. Next, considering the multi-stage nature and uncertainty of the degradation process, a semi-empirical physical degradation model is constructed, where stage identification is achieved using the Pruned Exact Linear Time (PELT) method, and uncertainty is estimated through Particle Filtering (PF). The Bayesian framework enables hybrid correction between the data-based and the model-based methods, integrating the strengths of both. Finally, experimental results demonstrate that the proposed method outperforms traditional models, effectively achieving more accurate quality predictions.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 1171-1191"},"PeriodicalIF":14.2,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902874","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":"Optimisation method for semiconductor wafer manufacturing system scheduling: Reinforcement learning with decision graph guiding","authors":"Da Chen ,&nbsp;Jie Zhang ,&nbsp;Lihui Wu ,&nbsp;Peng Zhang ,&nbsp;Youlong Lv ,&nbsp;Junliang Wang ,&nbsp;Hong Wang","doi":"10.1016/j.jmsy.2025.08.004","DOIUrl":"10.1016/j.jmsy.2025.08.004","url":null,"abstract":"<div><div>Semiconductor wafer fabrication, as a large-scale and complex discrete manufacturing system, presents significant challenges in shop floor scheduling due to its scale, uncertainty, and re-entrant processing. Additionally, effectively leveraging historical scheduling decision data remains a challenge, limiting the ability of scheduling algorithms to accurately assess the current system state. To address these issues, this paper proposes a reinforcement learning-based optimisation method guided by decision graphs. First, we introduce a multidimensional heterogeneous disambiguation graph to comprehensively represent the operational state of the wafer manufacturing system. Second, we design a graph neural network to characterise the multidimensional disambiguation graph and learn from historical decision-making experiences. Finally, we propose a decision graph-guided action strategy that optimises the reinforcement learning policy, improving training efficiency and the accuracy of action selection. Experimental results demonstrate that our method achieves superior generalisation performance and outperforms traditional approaches. This study provides an effective solution for optimising scheduling in semiconductor wafer manufacturing systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 1158-1170"},"PeriodicalIF":14.2,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902873","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":"Self-supervised time–frequency feature alignment for process monitoring of cyber–physical CNC machines","authors":"Yadong Xu ,&nbsp;J.C. Ji ,&nbsp;Yuxin Sun ,&nbsp;Sihan Huang ,&nbsp;Zhiheng Zhao ,&nbsp;George Q. Huang","doi":"10.1016/j.jmsy.2025.08.007","DOIUrl":"10.1016/j.jmsy.2025.08.007","url":null,"abstract":"<div><div>Self-supervised learning excels at uncovering latent features from incomplete data, thereby providing robust support for downstream applications. Capitalizing on this strength, a growing number of fault diagnosis models have been developed to monitor CNC machine tools, which are essential to modern manufacturing. These machines operate under demanding conditions – characterized by high speeds and heavy loads – and consequently generate mechanical signals with pronounced nonlinearity. Such inherent nonlinearity poses significant challenges for conventional feature extraction methods, necessitating advanced self-supervised techniques to effectively capture and interpret the underlying fault-related features for reliable condition monitoring. In this research, we introduce a self-supervised time–frequency feature alignment (STFA) algorithm for monitoring the manufacturing processes of industrial CNC machine tools. The STFA algorithm initially employs two domain-specific modules to extract time–frequency features from surveillance signals. A modern CNN is utilized to extract spatiotemporal information from the time domain, while a multi-scale CNN captures multi-granular features from the frequency domain. Subsequently, a dedicated time–frequency feature alignment module (TFAM) maps these features into a unified space, thereby exploiting their complementarity and enabling a more comprehensive representation. The STFA algorithm is trained through a dual-stage process—first, a pre-training phase to establish robust feature representations from unlabeled data, followed by a fine-tuning stage using a limited number of labeled samples to adapt the model for precise fault diagnosis. The effectiveness of the proposed STFA algorithm is validated using two manufacturing datasets collected from industrial CNC machine tools.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 1145-1157"},"PeriodicalIF":14.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896625","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":"Cerebrum twin: A 6D semantic digital twin of multi-lobe digital brain functions for human-centric Industry 5.0","authors":"Hanwei Teng ,&nbsp;Shuo Chen ,&nbsp;Changping Li ,&nbsp;Shujian Li ,&nbsp;Rendi Kurniawan ,&nbsp;Moran Xu ,&nbsp;Jielin Chen ,&nbsp;Tae Jo Ko","doi":"10.1016/j.jmsy.2025.08.009","DOIUrl":"10.1016/j.jmsy.2025.08.009","url":null,"abstract":"<div><div>Industry 5.0 highlights the need for human-centric and adaptive intelligence in smart manufacturing. This paper proposes the Cerebrum Twin (CT), a brain-inspired, six-dimensional semantic digital twin (SDT) system that unifies five human-like senses, including listening, speaking, reading, writing, and looking, within a cohesive multi-lobe digital brain framework. CT integrates real-time physical signals from force, vibration, and vision sensors by leveraging a synergistic ensemble of advanced artificial intelligence (AI) modules, such as Extreme Gradient Boosting (XGBoost), ConvNeXt V2, Efficient Sub-Pixel Convolutional Networks (ESPCN), stacked sparse autoencoder with supervision (SSAES), large language models (LLM), and reinforcement learning (RL). Uniquely, CT establishes a closed-loop semantic feedback mechanism, enabling dynamic perception, multimodal semantic abstraction, signal-driven prediction, adaptive parameter optimization, and intuitive voice-based human interaction. This holistic integration bridges the physical, semantic, and cognitive layers of CNC machining, supporting robust, transparent, and operator-oriented decision-making. The proposed system was validated through ultrasonic vibration-assisted blade dicing (UVABD) experiments. CT reduced dicing force prediction error by 39.86 %, improved tool wear prediction accuracy by 29.59 %, and decreased edge chipping severity by 60.47 % compared to the baseline model. These results demonstrate that a semantically empowered, multisensory digital twin (DT), enabled by real-time physical–semantic–AI fusion and human-in-the-loop optimization, can significantly enhance intelligent manufacturing performance and fulfill the vision of Industry 5.0.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 1125-1144"},"PeriodicalIF":14.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864294","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}