Journal of Manufacturing Systems最新文献

{"title":"Disassembly from scratch: An object-centric approach for robotic autonomous disassembly with zero contact/interference information","authors":"Yue Zang ,&nbsp;Xiazhen Xu ,&nbsp;Feiying Lan ,&nbsp;Yongquan Zhang ,&nbsp;Huayu Duan ,&nbsp;Marco Chacin ,&nbsp;Farzaneh Goli ,&nbsp;Roger Dixon ,&nbsp;Amir M. Hajiyavand ,&nbsp;Yongjing Wang","doi":"10.1016/j.jmsy.2025.12.013","DOIUrl":"10.1016/j.jmsy.2025.12.013","url":null,"abstract":"<div><div>Disassembly is important to circular economy, yet it remains challenging to be robotised due to the inherent uncertainty of end-of-life (EoL) products (e.g., corrosion, rust and missing part). A key challenge in robotising disassembly is that the interference information (e.g., spatial relations of components and assembly methods) is usually unavailable or inaccurate. To address this core problem, this paper presents an object-centric disassembly (OCD) framework, allowing robots to adapt dynamically to varying conditions without requiring prior knowledge of component contacts or interferences. In this framework, an OCD model is constructed in which individual disassembly tasks and their associated conditions are represented as modular units that are continuously refined through autonomous exploration. The performance of the framework is evaluated using a robotic platform integrating intelligent perception, planning, and execution modules for autonomous disassembly under uncertain environments. Experimental evaluations provide evidence that the proposed method enhances the flexibility and adaptability of robotic disassembly. Our approach and this new capability allow disassembly robots to handle real-world uncertainties effectively, eliminating the need for pre-defined interference information.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 314-332"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786607","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":"Human-centric manufacturing: Re-thinking, Re-justifying, and Re-envisioning","authors":"Xun Xu ,&nbsp;Tang Ji ,&nbsp;Pai Zheng ,&nbsp;Lihui Wang","doi":"10.1016/j.jmsy.2025.12.001","DOIUrl":"10.1016/j.jmsy.2025.12.001","url":null,"abstract":"<div><div>Human-Centric Manufacturing (HCM) stresses inclusion, resilience, and well-being. Recent studies focus on supporting workers on the factory floor, assuming that human presence in production will remain for the foreseeable future. Meanwhile, automation and artificial intelligence (AI) are rapidly transforming manufacturing and redefining human roles. This paper reviews automation trajectories, analyses the evolving roles of humans, and discusses the technological and social factors shaping future manufacturing. Our discussion suggests that human roles will decline and change, but not disappear anytime soon. HCM should evolve from focusing on physical presence to embedding human purpose in advanced and engaging manufacturing systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 259-268"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786612","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":"Semi-supervised and unsupervised learning for health indicator extraction from guided waves in aerospace composite structures","authors":"James Josep Perry ,&nbsp;Pablo Garcia-Conde Ortiz ,&nbsp;George Konstantinou ,&nbsp;Cornelie Vergouwen,&nbsp;Edlyn Santha Kumaran,&nbsp;Morteza Moradi","doi":"10.1016/j.jmsy.2025.12.014","DOIUrl":"10.1016/j.jmsy.2025.12.014","url":null,"abstract":"<div><div>Health indicators (HIs) are central to diagnosing and prognosing the condition of aerospace composite structures, enabling efficient maintenance and operational safety. However, extracting reliable HIs remains challenging due to variability in material properties, stochastic damage evolution, and diverse damage modes. Manufacturing defects (e.g. disbonds) and in-service incidents (e.g. bird strikes) further complicate this process. This study presents a comprehensive data-driven framework that learns HIs via two learning approaches integrated with multi-domain signal processing. Because ground-truth HIs are unavailable, a semi-supervised and an unsupervised approach are proposed: (i) a diversity deep semi-supervised anomaly detection (Diversity-DeepSAD) approach augmented with continuous auxiliary labels used as hypothetical damage proxies, which overcomes the limitation of prior binary labels and enables modelling of intermediate degradation, and (ii) a degradation-trend-constrained variational autoencoder (DTC-VAE), in which the monotonicity criterion is embedded via an explicit trend constraint. Guided waves with multiple excitation frequencies are used to monitor single-stiffener composite structures under fatigue loading. Time, frequency, and time–frequency representations are explored, and per-frequency HIs are fused via unsupervised ensemble learning to mitigate frequency dependence and reduce variance. Using fast Fourier transform features, the models achieved fitness scores of 81.6% (Diversity-DeepSAD) and 92.3% (DTC-VAE), indicating improved monotonicity and consistency over existing baselines. The proposed history-independent framework, supported by prognostic metrics–guided Bayesian optimisation and excitation frequency-agnostic HI fusion, enables the estimation of more robust HIs for aeronautical composite structures.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 468-492"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786670","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":"Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance","authors":"Jan-Phillip Herrmann ,&nbsp;Sven Tackenberg ,&nbsp;Tharsika Pakeerathan Srirajan ,&nbsp;Verena Nitsch","doi":"10.1016/j.jmsy.2025.12.020","DOIUrl":"10.1016/j.jmsy.2025.12.020","url":null,"abstract":"<div><div>In manufacturing systems with a job shop organization, queues between workstations create an intermittent process flow, allowing workers to schedule tasks entering the queue based on their needs and preferences. The resulting scheduling autonomy of individual workers often leads to inefficiencies in the overall production process due to the loss of control. Companies are therefore increasingly using algorithmic scheduling systems to assign task sequences to workers, thereby drastically reducing their autonomy and negatively affecting their job performance and well-being. This paper extends the existing flexible job shop scheduling problem by sequencing preferences (FJSPSP) to incorporate a human-centered perspective by predicting workers’ task sequencing decisions using learning-to-rank (LTR) methods. By learning workers’ individual task sequencing preferences, it becomes possible to predict the processing sequence based on task characteristics. The scheduling algorithm for the FJSPSP presented in the paper incorporates workers’ learned sequencing preferences as constraints. Considering workers’ learned task sequencing decisions, the FJSPSP optimizes only task assignments to maintain workers’ autonomy over task sequences. The contributions of this paper are fourfold, namely, (1) presenting an approach to elicit sequencing decision datasets from workers, (2) demonstrating the successful prediction of humans’ and an actual worker’s task sequencing decisions with LTR, (3) formulating the FJSPSP variant that integrates workers’ sequencing preferences as constraints and proving its effectiveness in a simulation study, and (4) consolidating these steps into an explainable artificial intelligence (XAI)- and LTR-enabled sociotechnical system design framework. The paper closes with a discussion of the overall methodology and future research perspectives.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 541-560"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836452","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":"A spatio-temporal parallel ensemble learning approach for operation situation prediction in discrete manufacturing workshop","authors":"Sai Geng ,&nbsp;Yu Guo ,&nbsp;Weiwei Qian ,&nbsp;Weiguang Fang ,&nbsp;Shengbo Wang ,&nbsp;Shaohua Huang ,&nbsp;Xiaoyu Hou","doi":"10.1016/j.jmsy.2025.12.022","DOIUrl":"10.1016/j.jmsy.2025.12.022","url":null,"abstract":"<div><div>In the complex and dynamic discrete manufacturing environment, accurate prediction of the workshop operation situation (WOS) is crucial to ensure on-time delivery of orders. However, the spatio-temporal (ST) coupling characteristics of the manufacturing process, dynamic fluctuations of workshop performance, and varying contributions of samples to the prediction model make WOS prediction more challenging. To address these issues, this paper proposes a ST parallel ensemble learning approach for WOS prediction. Specifically, based on workshop production data, a temporal data model and a dynamic graph model are constructed to comprehensively characterize the ST characteristics of the production process. Subsequently, this paper proposes a ST parallel ensemble learning method, named Adaboost-GLT, which integrates three ST weak learners (GCN, LSTM, and TGCN) to effectively capture the ST characteristics. Furthermore, a dynamic optimal selection mechanism is designed to adaptively select the best-performing weak learner at each stage, enabling the prediction method to evolve synchronously with the dynamic changes of the manufacturing process. Additionally, a sample weight updating strategy that takes into account sample timeliness and prediction error is introduced to improve the rationality of Adaboost-GLT's attention allocation to samples during training. Finally, the performance of Adaboost-GLT is experimentally validated on real workshop production datasets. The experimental results show that Adaboost-GLT can fully exploit the ST characteristics, effectively cope with the dynamic fluctuations of workshop performance, and thereby achieve high-precision prediction of WOS.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 584-600"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836450","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":"AR-assisted human-robot collaborative assembly system: Integrating visual language model and deep reinforcement learning for task planning and seamless interactive guidance","authors":"Changchun Liu ,&nbsp;Dunbing Tang ,&nbsp;Haihua Zhu ,&nbsp;Zequn Zhang ,&nbsp;Liping Wang ,&nbsp;Qingwei Nie","doi":"10.1016/j.jmsy.2025.11.019","DOIUrl":"10.1016/j.jmsy.2025.11.019","url":null,"abstract":"<div><div>In the human-oriented context of Industry 5.0, human-robot collaboration (HRC) has become the core driving force for innovating the production model of assembly systems by integrating human flexibility ability with the precision and repeatability of robots. As the core link to achieve efficient collaboration, task planning is facing multiple challenges in dynamic and complex scenarios. On the one hand, it is hard to perform cognition of multimodal human-robot-environment in HRC assembly scenarios. On the other hand, heterogeneous capabilities of humans and robots (e.g., flexible decision-making by humans and precise execution by robots) are hard to be fully used to achieve reasonable task allocation and timing optimization in dynamic and complex HRC assembly scenarios. To address these issues, a Vision Language Model (VLM)-enhanced deep reinforcement learning-driven task planning approach is proposed towards Augmented Reality (AR)-assisted HRC assembly system. Firstly, a self-trained VLM is proposed through the integration of domain-specific knowledge and real-time situational data to enable context-aware in HRC assembly system. Through the fine-tuning of role configuration parameters for the pre-constructed VLM via prompt engineering, VLM can possess cognition of multi-dimensional assembly scenario elements. Reinforcement learning model can be endowed with the eyes to perceive HRC assembly scenarios through VLM-enhanced cognition of the dynamic HRC environment. Based on the VLM-enhanced cognition of the dynamic HRC environment, an improved multi-agent reinforcement learning-based HRC assembly task planning model is established to achieve humanized task planning, which can consider the competitive relationship between humans and robots with multi-agent conflict mechanism. Based on the HRC assembly task planning result, AR can enable operators to accomplish visual HRC assembly guidance through virtual-real mapping of HRC assembly information (e.g., HRC assembly procedures) and interact seamlessly with the VLM. Finally, experimental results show that the proposed method can improve the efficiency and well-being of HRC in human-centric assembly systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 40-67"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682161","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":"Sustainability assessment for machining processes based on progressive analysis of critical sources","authors":"Junjie Hu ,&nbsp;Wei Zhao ,&nbsp;Liang Li ,&nbsp;Ning He ,&nbsp;Muhammad Jamil ,&nbsp;Aqib Mashood Khan ,&nbsp;Chao Wang ,&nbsp;Xiaowei Zheng","doi":"10.1016/j.jmsy.2025.12.002","DOIUrl":"10.1016/j.jmsy.2025.12.002","url":null,"abstract":"<div><div>The machining processes must achieve sustainability due to growing ecological concerns and energy crises. As an effective tool, sustainability assessment guides the implementation of sustainable strategies in machining processes. However, the complex resource consumption across machining processes and the coupling effects among machining parameters have greatly hindered its implementation. To address this challenge, this study proposes a sustainability assessment method based on progressive analysis of critical sources, enabling the identification and evaluation of key factors influencing sustainability. First, critical sources are identified through quantification of contribution degrees and sensitivity analysis. Progressive analysis is employed to focus resources on in-depth research into the fundamental characteristics and operational mechanisms of critical sources, establishing specialized indicators such as specific embodied energy and specific carbon emissions for cutters. Subsequently, a sustainable soft sensor is developed to enable efficient and cost-effective sustainability assessment. Finally, a milling case study incorporating various tool types and cooling-lubrication strategies demonstrates the method’s effectiveness in comprehensively capturing the coupling effects inherent in machining processes. The results confirm the method’s reliability and clearly validate its capability to evaluate sustainability performance in machining. This study not only provides technical support for sustainability assessments but also delivers actionable insights to facilitate the implementation of sustainable machining strategies.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 289-313"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786667","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":"PI-KAF: A physics-informed constrained online interpretable monitoring method for tool wear","authors":"Liguo Zhang ,&nbsp;Qinghua Song ,&nbsp;Haifeng Ma ,&nbsp;Zhanqiang Liu","doi":"10.1016/j.jmsy.2025.11.024","DOIUrl":"10.1016/j.jmsy.2025.11.024","url":null,"abstract":"<div><div>Deep learning-based tool wear monitoring methods often suffer from poor interpretability, parameter redundancy, and limited cross-domain generalization, especially under variable operating conditions and when only small sample sizes are available. These limitations hinder their practical deployment in practical manufacturing environments. To overcome these challenges, this study proposes a highly interpretable and lightweight tool wear monitoring framework tailored for small-sample, variable-condition scenarios. The method randomly extracts fixed-length segments from vibration signals to construct small-sample datasets, utilizing both signal envelope spectrum and cutting time as input features. Guided by the degradation law of tool wear, explicit physical constraints are imposed on the solution space of a Kolmogorov‑Arnold Fourier neural network, yielding a physics‑informed data‑driven model. SHAP analysis is employed to quantify the contribution of each feature, enhancing model transparency. Validation on public datasets under both single‑ and multi‑condition settings demonstrates that the proposed method delivers excellent performance across diverse operating conditions, achieving a stable prediction R² of up to 95 %, an inference latency of only 2 ms, and a reduction of approximately 90 % in model parameters. This solution can be integrated into the edge computing platform of CNC systems, making it particularly suitable for machining scenarios with high real-time requirements. It offers a lightweight, precise, and efficient monitoring capability for smart factories, contributing simultaneously to improvements in product quality and manufacturing efficiency.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 135-151"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786760","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":"An extended graphical method with reinforcement learning for distributed job shop scheduling problems with transfers","authors":"Lin Huang ,&nbsp;Donglin Wang ,&nbsp;Shikui Zhao","doi":"10.1016/j.jmsy.2025.12.007","DOIUrl":"10.1016/j.jmsy.2025.12.007","url":null,"abstract":"<div><div>Modern manufacturing often requires collaboration across multiple factories due to the dispersion of specialized equipment and differences in processing capacity. In such scenarios, jobs must be processed across different sites, and cross-factory transfers significantly impact scheduling performance. This paper studies the distributed job shop scheduling problem with transfers and proposes an Extended Graphical Method with Reinforcement Learning (EGMRL) to effectively address it. The main innovations of EGMRL are fourfold. First, a transfer-zone is introduced into the graphical method, enabling explicit modeling of both intra-factory and inter-factory transportation times. Second, a layered path search algorithm is developed to accelerate path exploration, thereby improving computational efficiency while maintaining accuracy. Third, a Q-learning–based adaptive strategy dynamically guides job deletion and reinsertion according to the inter-factory state, enhancing adaptability across different problem scales. Finally, a tabu search module is integrated as a local improvement strategy to refine factory-level schedules and prevent premature convergence. Comprehensive experiments on 240 extended benchmark instances and a real-world engineering case study demonstrate that EGMRL consistently outperforms four competitive algorithms in terms of solution quality and stability. Furthermore, the results suggest that the extended graphical method provides promising new solution approaches for tackling scheduling problems with other practical constraints, such as worker–machine collaboration and sequence-dependent setup times.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 426-450"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786605","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":"LLM-enhanced embodied multi-agent manufacturing system: A novel self-organizing production paradigm for embodied perception, embodied analysis and embodied decision","authors":"Changchun Liu ,&nbsp;Dunbing Tang ,&nbsp;Haihua Zhu ,&nbsp;Liping Wang ,&nbsp;Qixiang Cai ,&nbsp;Qingwei Nie","doi":"10.1016/j.jmsy.2025.12.016","DOIUrl":"10.1016/j.jmsy.2025.12.016","url":null,"abstract":"<div><div>As the manufacturing industry evolves towards greater intelligence and flexibility, multi-agent manufacturing systems encounter critical challenges, including frequent dynamic disruptions, inefficient inter-agent collaboration, and underutilized manufacturing knowledge. To address these issues, this paper proposes an LLM-enhanced embodied multi-agent system that integrates embodied perception, analysis, and decision-making to establish a novel self-organizing production paradigm. First, an LLM-driven embodied machine agent is developed. Through the precise mapping of physical entities to internal functional modules, these agents are endowed with the capability for context-aware comprehension of manufacturing domain information. Second, an LLM-enhanced multimodal embodied perception mechanism is designed. By deeply integrating continuous data acquisition with implicit domain knowledge, this mechanism equips the system with the sensory capabilities necessary to capture dynamic disturbances in real time. Building on this, an LLM-driven embodied analysis method is developed for dynamic disturbances. This method, which involves systematic data preprocessing, domain knowledge-integrated multimodal data correlation analysis, and predictive model construction, forms the system’s core ability to identify production schedule anomalies and predict failure trends. Finally, an LLM-integrated embodied decision-making framework is established. This framework balances local autonomy with global goal consensus through self-organizing negotiation and dynamic game mechanisms. It further integrates human problem-solving expertise with the efficiency of machine intelligence to generate optimal production strategies, thereby providing the foundational support for high-level autonomous collaboration among embodied agents. Experimental results demonstrate that this self-organizing mode supported by LLM-enhanced embodied agents can achieve a faster response speed than conventional decision-making methods (e.g., Deep Q-Network) and standalone LLMs (e.g., GPT-4). The proposed system effectively overcomes bottlenecks throughout the perception-analysis-decision-making process, successfully establishing a novel self-organizing production paradigm.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 357-382"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786608","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}