Journal of Manufacturing Systems最新文献

{"title":"Metallurgy-guided bidirectional generative framework for performance prediction and inverse process design in hot-rolled steel","authors":"Pengju Xu,&nbsp;Keming Hu,&nbsp;Yuchun Wu,&nbsp;Haodong Zhang,&nbsp;Zhimin Lv,&nbsp;Zhiyan Zhang,&nbsp;Yiquan An,&nbsp;Qian Sun,&nbsp;Xiang He","doi":"10.1016/j.jmsy.2025.08.020","DOIUrl":"10.1016/j.jmsy.2025.08.020","url":null,"abstract":"<div><div>To effectively handle the complex industrial data characteristics inherent in hot-rolled steel manufacturing, this paper proposes a Cycle-Consistent Bidirectional Generative Framework with Metallurgical Priors (C2-BIGF). Focusing on hot-rolled steel strips, the framework leverages an enhanced variational autoencoder (VAE) integrated with metallurgical priors for informed feature engineering and representation learning. Key process features are quantitatively extracted via physically-based microstructural evolution equations and are systematically integrated into both model input and solution validation, thereby embedding metallurgical prior knowledge throughout the entire modeling framework. The proposed approach adopts a bidirectional generative structure combined with a cycle-consistency mechanism, significantly enhancing prediction stability and structural coherence between forward property prediction and inverse process generation. Comprehensive validation is conducted using real-world industrial datasets, including rigorous ablation studies evaluating individual module contributions. Additionally, two practical industrial scenarios are presented: inverse generation of process parameters from desired mechanical properties under fixed alloy compositions, and cost-optimized alloy composition design under fixed process constraints. In each scenario, the feasibility and rationality of generated solutions are critically evaluated through metallurgical prior knowledge and cycle-consistency verification. Experimental results validate the framework's effectiveness. In forward prediction, it achieves an overall R² of 0.9689. In inverse design, it reliably generates solutions for target yield strengths within a ± 15 MPa margin and successfully produces cost-optimized alloy designs, effectively supporting customized and flexible steel production.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 134-156"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046930","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":"2025-12-01","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":"Generative AI-powered planning: A hybrid graph-diffusion approach for demand-driven flexible manufacturing systems","authors":"Chen Li,&nbsp;Qing Chang","doi":"10.1016/j.jmsy.2025.08.016","DOIUrl":"10.1016/j.jmsy.2025.08.016","url":null,"abstract":"<div><div>Flexible Smart Manufacturing Systems (FSMS) are critical to achieving mass customization and operational agility under Industry 4.0. However, planning effective FSMS configurations remains challenging due to fluctuating market demands, heterogeneous system components, complex interdependencies, and the need to optimize resource utilization. Conventional planning methods often require predefined line configurations and lack adaptability, scalability, and awareness of dynamic system properties. This paper presents a novel Hybrid Graph-Diffusion Based Planning Framework that integrates generative AI with system-theoretic modeling to autonomously generate optimal FSMS configurations based on different market demands. Specifically, we introduce a system model-embedded Heterogeneous Graph (HG) to represent the structure and properties of an FSMS and infuse it within a system property-tailored diffusion model to generate reconfigurable plan configurations. The final system property-guided refinement guarantees that the final plan configuration is optimal in both demand satisfaction and resource use. Furthermore, our ablation studies validate that our framework significantly outperforms conventional approaches in both demand satisfaction and resource efficiency. Furthermore, our ablation studies validate the effectiveness of the system property guidance and HG-based representation in enhancing planning feasibility, robustness, and adaptability.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 175-195"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096553","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 unified V-shaped digital twin modeling paradigm of aircraft assembly systems for improving modeling accuracy and assembly quality","authors":"Ruihao Kang ,&nbsp;Junshan Hu ,&nbsp;Xingtao Su ,&nbsp;Zhengping Li ,&nbsp;Zhanghu Shi ,&nbsp;Wei Tian","doi":"10.1016/j.jmsy.2025.09.013","DOIUrl":"10.1016/j.jmsy.2025.09.013","url":null,"abstract":"<div><div>Digital Twin (DT) technology is one of the key approaches to enhancing the intelligence of aircraft assembly equipment. However, the diversity of such equipment types and significant structural differences present substantial challenges to the development of DT models. This article proposes a unified V-shaped DT modeling paradigm to support high-accuracy and structured modeling. The robotic drilling system is used as an example to validate this paradigm. The modeling requirements of this system are established based on a comprehensive analysis of its structural characteristics and operational tasks. A corresponding virtual entity is constructed through parametric modeling based on kinematic analysis. The behavior model represents the interaction protocols and decision logic of the physical system, with basic modules for communication and behavioral analysis. These modules are then systematically integrated to form a complete task model for drilling. The structural validation of the virtual entity is performed, accompanied by the formulation of behavioral matching degree and task execution consistency to evaluate the effectiveness of the proposed modeling paradigm. Meanwhile, kinematic parameter identification is integrated to calibrate the virtual entity, thereby further enhancing the DT modeling accuracy. The experimental results show that the behavior matching degree for positioning after calibration is 0.204 ± 0.228 mm, with an increase of 78.71 %. The average errors of hole position and diameter are reduced by 78.43 % and 14.27 %, respectively, after calibration. The corresponding task execution consistency is improved to 1.465 and 1.462. This indicates that the high-accuracy DT model constructed by the proposed paradigm effectively enhances the intelligence and assembly quality of the equipment.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 372-391"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155081","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":"Operational resilience of additively manufactured parts to stealthy cyberphysical attacks using geometric and process digital twins","authors":"Jeremy Cleeman ,&nbsp;Adrian Jackson ,&nbsp;Anandkumar Patel ,&nbsp;Zihan Wang ,&nbsp;Thomas Feldhausen ,&nbsp;Chenhui Shao ,&nbsp;Hongyi Xu ,&nbsp;Rajiv Malhotra","doi":"10.1016/j.jmsy.2025.10.009","DOIUrl":"10.1016/j.jmsy.2025.10.009","url":null,"abstract":"<div><div>Cyberphysical attacks on the digital backbone of Additive Manufacturing (AM) can compromise the printed part’s functionality. They can alter features in the digital geometry to introduce geometric defects (e.g., missing fillets) or alter process parameters to create local defects (e.g., voids). Addressing the downtime, waste, and quality deterioration associated with existing solutions requires operational resilience, i.e., rapid elimination or disruption of defect formation (to retain part function) without production stoppage or part disposal (to retain yield). This need is unmet due to the inherently unpredictable nature of attack-induced alterations, lack of access to the original geometric model for identification of altered geometric features, and in-process imposition of unknown process dynamics via attack-driven alteration of real-time-uncontrolled (or exogenous) parameters. This work establishes the above-mentioned operational resilience for the first time by creating two Digital Twins (DT). The Geometric DT (Geo-DT) is based on a unique physical-field-driven soft sensor and topology optimization method. The Process Digital Twin (Pro-DT) combines local defect quantification with a novel Reinforcement Learning formulation and training method. The importance of these methodological advances and the scalability of our approach are examined on a real AM testbed. It is shown that Geo-DT can correct geometric defects without access to the original digital geometry or explicit knowledge of attack-altered geometric features. Further, Pro-DT can accelerate real-time disruption of local defects despite attack-driven imposition of unknown process dynamics. We discuss how our framework goes beyond the contemporary focus on pre-attack security and in-attack detection towards resilience for AM and beyond.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 626-651"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416648","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":"Production scheduling for human–robot collaborative assembly workstations under constraints of ergonomic fatigue and simultaneous cooperation","authors":"Kyu-Tae Park ,&nbsp;Chiho Lim ,&nbsp;Ju-Yong Lee","doi":"10.1016/j.jmsy.2025.09.012","DOIUrl":"10.1016/j.jmsy.2025.09.012","url":null,"abstract":"<div><div>Human–robot collaboration (HRC) is a key enabler of human-centric manufacturing, achieved through cooperation between human operators and collaborative robots. HRC can be classified into three developmental phases: coexistence, sequential collaboration, and simultaneous cooperation. To address ergonomic fatigue and simultaneous cooperation (HRCAW-ES) constraints, this study introduces a novel scheduling model that integrates sequential collaboration and simultaneous cooperation, focusing on production scheduling in shared HRC assembly workstations involving one human operator and one collaborative robot. This setting accounts for key operational constraints, including operation precedence and assembly relationships, human task eligibility based on ergonomic risk factors, ergonomic fatigue accumulation and recovery following established models, sequence-dependent setup for end-effector switching on a collaborative robot, and simultaneous cooperation between the two collaborators. A mathematical model was developed to formulate an adaptive variable neighbourhood search (AVNS) algorithm and a disjunctive graph representation was employed to analyse the structural characteristics of the HRCAW-ES. An ablation study performed using both linear and nonlinear fatigue models revealed the superior performance of the proposed AVNS algorithm compared to the control group across various scenarios involving varying cooperation ratio and fatigue levels. This experiment includes results obtained using parameters collected from the small-product packaging and cable-assembly processes. Emphasis was placed on examining the impacts of ergonomic limitations and simultaneous cooperation within the scheduling framework. The proposed method generates high-quality, feasible schedules to address the complexity introduced by ergonomic constraints and cooperative requirements. The method may be extendable to a wide range of assembling processes where full automation is infeasible.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 337-356"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155082","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":"Modeling stochastic dynamics of manufacturing processes with manifold signals: A harmonic analysis approach with NP-ODEs","authors":"Yu Wang ,&nbsp;Bo Yang ,&nbsp;Shilong Wang ,&nbsp;Zhengping Zhang ,&nbsp;Yucheng Zhang ,&nbsp;Haijian Liu","doi":"10.1016/j.jmsy.2025.08.010","DOIUrl":"10.1016/j.jmsy.2025.08.010","url":null,"abstract":"<div><div>Modern manufacturing increasingly leverages intricate signals that reflect the complex geometries of produced parts. These signals hold vital insights into the quality and working status of the underlying manufacturing systems. While conventional deep learning approaches excel with structured data like time series processing or image vision tasks, they can struggle to model these geometrically complex signals, which are inherently in a non-Euclidean manifold domain and exhibit stochastic dynamical evolution behaviors over time. In this paper, we present a novel methodology that leverages the principles of harmonic analysis with the potential of continuous-time dynamics modeling and stochastic process representation. Specifically, our contributions are twofold: (1) we present a general and flexible framework for modeling the stochastic dynamics of manifold signals within manufacturing processes. This framework uniquely synergizes Neural ODEs and Neural Processes (NPs), enhanced by a neural numerical integration scheme for computational efficiency; (2) we develop a rigorous and tailored representation approach rooted in harmonic analysis, enabling the construction of learnable wavelet filters for multiscale pattern analysis on manifold signals, with Geometric Deep Learning (GDL) principles ensuring compatibility with modern deep learning architectures. We comprehensively validate our methodology through simulations and a real-world automotive case study. Results demonstrate its effectiveness in modeling the complex stochastic dynamics inherent in manifold signals found in practical manufacturing settings, highlighting its potential for industrial applications.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 12-28"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934094","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 proactive integrated design paradigm of intelligent manufacturing system towards Industry 5.0","authors":"Shuo Zhang ,&nbsp;Hanwu He ,&nbsp;Xian Yang","doi":"10.1016/j.jmsy.2025.09.002","DOIUrl":"10.1016/j.jmsy.2025.09.002","url":null,"abstract":"<div><div>With the ongoing transition from Industry 4.0 to Industry 5.0, intelligent manufacturing systems (IMS) based on machine vision are increasingly prioritizing both human-centric principles and proactive integrated design. However, existing systems often face challenges in effectively balancing technical performance with the integration of human factors. To address these challenges, this paper proposes a human-centric proactive IMS integration framework aligned with Industry 5.0 objectives. The framework is structured around three key synergistic steps: First, hierarchical task decomposition: Complex production tasks are subdivided into essential subtasks such as calibration, positioning. Second, human-centric design: Human factors are systematically embedded in each subtask through unified design frameworks or computational models. Third, proactive integrated design: enhances autonomous collaboration is achieved through the proactive integrated design of communication protocols, establishing autonomous interactions between IMS modules to enable intelligent cooperation. The experimental results demonstrate that the proposed framework not only retains the technical characteristics of high precision and stability from Industry 4.0, but also incorporates the human-centric usability required for Industry 5.0. This approach extends Industry 4.0 foundations and provides a scalable pathway toward Industry 5.0 development through human-centric proactive integration.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 65-81"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020028","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":"Deep reinforcement learning for event-driven predictive–reactive multi-objective scheduling in dynamic flexible job shop","authors":"Chunyan Duan,&nbsp;Yuxin Mo,&nbsp;Zehao Zhang","doi":"10.1016/j.jmsy.2025.11.014","DOIUrl":"10.1016/j.jmsy.2025.11.014","url":null,"abstract":"<div><div>In the era of intelligent manufacturing, dynamic scheduling problems characterized by high variability and complexity mandate prompt solutions. Deep Reinforcement Learning (DRL) is particularly effective for addressing these challenges through their formulation as Markov Decision Processes (MDPs). However, most DRL-based dynamic scheduling methods primarily adopt reactive strategies, thereby restricting their capacity to achieve global optimality. This paper addresses the Dynamic Multi-Objective Flexible Job Shop Scheduling Problem (DMFJSP) by introducing a comprehensive event-driven predictive–reactive scheduling framework designed to balance computational efficiency and solution quality. The approach employs a deterministic scheduling model that integrates the proposed Multi-Objective Hybrid Deep Q-Network (MOHDQN) as both the schedule generator and the baseline, enabling adaptive adjustment of objective weights. The Markov Jump Decision Process (MJDP) is incorporated to better manage event-driven predictive–reactive scheduling. Furthermore, seven rescheduling rules are developed to update schedules through a hybrid deep recurrent Q-network in response to dynamic events. Extensive numerical experiments across diverse problem scales demonstrate that the proposed method achieves a favorable balance between scheduling performance and computational efficiency. Finally, the most suitable rescheduling strategies for different enterprises are analyzed with respect to their specific operational complexities, and future development directions are also discussed.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 992-1008"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568614","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 approach based on hybrid-augmented intelligence for the combination and optimization of human-machine teams","authors":"Liu Xinyu ,&nbsp;Yuan Bingkun ,&nbsp;Wang Pengchao ,&nbsp;Ding Ning ,&nbsp;Chu Jianjie","doi":"10.1016/j.jmsy.2025.09.010","DOIUrl":"10.1016/j.jmsy.2025.09.010","url":null,"abstract":"<div><div>Recent advances in Large Language Models (LLMs) have demonstrated their unparalleled capability in collaborative design requirements mining, offering significant potential for the integration of Human–Machine Teams (HMTs) and improved efficiency in design mining processes. However, existing approaches often lack integrated frameworks capable of simultaneously addressing both the compositional and operational challenges of LLM–human teams, which hinders their effective deployment in complex, real-world scenarios. Specifically, two critical challenges remain: first, how to effectively transform LLMs into reliable domain experts capable of understanding and elaborating design requirements; and second, how to optimize HMT configurations amid inherent ambiguities in human expert evaluations. To address these gaps, we propose a novel dual-paradigm Hybrid-Augmented Intelligence (HAI) framework that integrates Cognitive Computing (CC-HAI) with Human-in-the-Loop (HITL-HAI) mechanisms. Our key contributions include a CC-HAI–based cognitive teammate mechanism that uses structured prompt engineering to transform LLMs into domain-specialized roles, facilitating the formation of collaborative HMTs; and an HITL-HAI uncertainty mitigation method that employs a Z-number-enhanced cloud modeling approach to manage subjective uncertainties in expert assessments and support robust team configuration. The framework is validated through multi-domain case studies spanning smart home systems, smart cockpits, medical devices, and baby products. Extensive experiments demonstrate its effectiveness in terms of team performance, error reduction, cross-domain generalizability, and decision-making superiority. This research provides a replicable paradigm for deploying LLMs as cognitive collaborators in collaborative design ecosystems, contributing to both theory and methodology in human–machine team intelligence.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 306-321"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118788","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}