Journal of Manufacturing Systems最新文献_第3页

A hybrid multiobjective particle swarm optimization with Q-learning-driven local search for distributed heterogeneous hybrid flow-shop scheduling problem with worker–machine–environment collaboration 基于q -学习驱动局部搜索的混合多目标粒子群算法求解工人-机器-环境协同的分布式异构混合流水车间调度问题

IF 14.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2026-04-01 Epub Date: 2025-12-31 DOI: 10.1016/j.jmsy.2025.12.024

Wenqiang Zhang , Sen Yang , Mingzhe Li , Yashuang Mu , Guohui Zhang , Mitsuo Gen , Peng Li

{"title":"A hybrid multiobjective particle swarm optimization with Q-learning-driven local search for distributed heterogeneous hybrid flow-shop scheduling problem with worker–machine–environment collaboration","authors":"Wenqiang Zhang , Sen Yang , Mingzhe Li , Yashuang Mu , Guohui Zhang , Mitsuo Gen , Peng Li","doi":"10.1016/j.jmsy.2025.12.024","DOIUrl":"10.1016/j.jmsy.2025.12.024","url":null,"abstract":"<div><div>The distributed heterogeneous hybrid flow-shop scheduling problem (DHHFSP) involves geographically dispersed factories, heterogeneous machines, and skilled workers, leading to complex multiobjective scheduling challenges. Existing studies usually ignore the critical role of workers in distributed manufacturing, and conventional multiobjective optimization algorithms struggle to balance convergence and solution diversity. To address these gaps, this paper develops a worker–machine–environment collaborative model for DHHFSP that simultaneously minimizes makespan, total energy consumption, and total worker cost, and proposes a hybrid multiobjective particle swarm optimization algorithm with Q-learning-driven local search (HMOPSO-QLS). The proposed approach features a hybrid framework that combines multidirectional particle swarm global search with reinforcement learning-based local search, a Pareto front oriented four directional swarm decomposition and update mechanism where three boundary exploration sub-swarms and one central exploration sub-swarm cooperatively guide particle evolution, and a two level local search scheme that integrates domain knowledge based inter-factory load balancing with Q-learning based adaptive intra-factory variable neighborhood search. Comprehensive experiments demonstrate that HMOPSO-QLS significantly outperforms classical multiobjective optimization algorithms in solving DHHFSP in terms of convergence and solution diversity. In distributed human–machine collaborative manufacturing, the proposed framework and algorithm support more effective configuration of factories, machines, and workers, providing robust schedules that are directly applicable to practical production decision-making and cross-factory coordination.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 1-21"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145852447","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}

引用次数: 0

A literature review on deep reinforcement learning for machine scheduling problems 机器调度问题中深度强化学习的文献综述

IF 14.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1016/j.jmsy.2025.12.017

Constantin Waubert de Puiseau , Furkan Ercan , Jannik Peters , Marvin Brune , Hasan Tercan , Christopher Prinz , Tobias Meisen , Bernd Kuhlenkötter

{"title":"A literature review on deep reinforcement learning for machine scheduling problems","authors":"Constantin Waubert de Puiseau , Furkan Ercan , Jannik Peters , Marvin Brune , Hasan Tercan , Christopher Prinz , Tobias Meisen , Bernd Kuhlenkötter","doi":"10.1016/j.jmsy.2025.12.017","DOIUrl":"10.1016/j.jmsy.2025.12.017","url":null,"abstract":"<div><div>Machine scheduling represents a core challenge in industrial production systems due to its inherently complex combinatorial nature and its critical role in enhancing operational efficiency. With recent advances in artificial intelligence, deep reinforcement learning (DRL) has gained increasing attention as an innovative tool to address scheduling tasks with a self-adapting, data-driven approach. This survey presents a comprehensive review of 143 publications between 2018 and February 2025 that apply DRL to machine scheduling problems. We develop a structured framework to classify and compare problem settings, algorithmic designs, and evaluation methodologies. Key aspects such as action and observation space design, reward functions, neural network architectures, and experimental benchmarks are systematically analyzed. The review identifies current trends, outlines promising patterns, and highlights open research opportunities for DRL-based scheduling solutions. The goal of this survey is to make the rapidly evolving research landscape more accessible to both academics and practitioners and to identify the next steps in research and application. To facilitate reproducible research and customized analysis, we publish the dataset underpinning this review, which includes 61 annotated features per publication, allowing for customizable filtering and further in-depth exploration of niches within the field. This dataset is publicly accessible <span><span>online</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 96-126"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979110","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}

引用次数: 0

Multiple batches scheduling for dynamic reconfigurable seru production system considering fatigue effect 考虑疲劳效应的动态可重构血清生产系统多批次调度

IF 14.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2026-04-01 Epub Date: 2026-02-09 DOI: 10.1016/j.jmsy.2026.01.013

Jun Guo , Zhi Liu , Hui Zhang , Baigang Du , Kaipu Wang , Lei Wang , Yibing Li

{"title":"Multiple batches scheduling for dynamic reconfigurable seru production system considering fatigue effect","authors":"Jun Guo , Zhi Liu , Hui Zhang , Baigang Du , Kaipu Wang , Lei Wang , Yibing Li","doi":"10.1016/j.jmsy.2026.01.013","DOIUrl":"10.1016/j.jmsy.2026.01.013","url":null,"abstract":"<div><div>In the era of mass customization, customers' demands for products are characterized by diversity, small batches and short delivery cycles, which has drawn increasing attention to the <em>seru</em> production system. However, existing research on the scheduling problems of <em>seru</em> production systems has largely concentrated on static <em>seru</em> systems, which are insufficient to meet the people-oriented and flexible demands of modern manufacturing modes. Therefore, this paper establishes the scheduling problem for multi-batch reconfigurable <em>seru</em> production system (SPMRSPS) considering workers’ fatigue effect. A mixed-integer programming model is constructed with minimizing makespan, total idle time of multi-skilled workers and total storage cost simultaneously. And a multi-objective adaptive evolutionary algorithm with heuristic strategy (MOAEH) is proposed to solve it. In MOAEH, for the characteristics of SPMRSPS, a mapping coding scheme is designed. And an objective value calculation method based on dynamic incremental evaluation is proposed according to dynamic reconfigurability. Additionally, several adaptive genetic operators are designed to enhance the global search capability of the algorithm. Finally, numerical results by comparing MOAEH with the well-known algorithms verify the effectiveness of MOAEH. This study provides managers with an efficient approach to addressing the SPMRSPS problem.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 402-418"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187924","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}

引用次数: 0

A decomposition-based optimization method for parallel machine scheduling under quality uncertainty in wafer manufacturing 基于分解的晶圆制造质量不确定条件下并行机器调度优化方法

IF 14.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2026-04-01 Epub Date: 2026-02-06 DOI: 10.1016/j.jmsy.2026.02.002

Sang-Wook Lee , Dong-Ha Lee , Min-Chan Kim , So-Young Lee , Hyun-Jung Kim

{"title":"A decomposition-based optimization method for parallel machine scheduling under quality uncertainty in wafer manufacturing","authors":"Sang-Wook Lee , Dong-Ha Lee , Min-Chan Kim , So-Young Lee , Hyun-Jung Kim","doi":"10.1016/j.jmsy.2026.02.002","DOIUrl":"10.1016/j.jmsy.2026.02.002","url":null,"abstract":"<div><div>We address a parallel machine scheduling problem where the quality of each job after processing is uncertain but depends on both the job and the machine to which it is assigned. In the problem, switching between different job families on a machine requires a setup, which incurs additional time, and there are deadlines for jobs. The objective is to minimize the number of setups while ensuring all jobs meet their deadlines and quality constraints. This problem is motivated by real wafer manufacturing environments, and we propose an exact algorithm based on Branch-and-Check that separates job assignment and sequencing. The master problem assigns jobs to machines while minimizing setups and ensuring quality constraints, whereas the subproblem optimizes sequencing while satisfying deadline constraints. To enhance computational efficiency, we introduce pattern-based methods that eliminate redundant job assignments and restrict unnecessary searches. Computational experiments demonstrate that the proposed algorithm outperforms the integer programming model, the dispatching rule, and the genetic algorithm. A real-world case study further validates its effectiveness in practical wafer manufacturing environments.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 386-401"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187922","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}

引用次数: 0

An inspection path planning approach based on hierarchical reinforcement learning 一种基于分层强化学习的检测路径规划方法

IF 14.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI: 10.1016/j.jmsy.2025.12.027

Kuanhong Yan , Daxin Liu , Zhenyu Liu , Jianrong Tan

{"title":"An inspection path planning approach based on hierarchical reinforcement learning","authors":"Kuanhong Yan , Daxin Liu , Zhenyu Liu , Jianrong Tan","doi":"10.1016/j.jmsy.2025.12.027","DOIUrl":"10.1016/j.jmsy.2025.12.027","url":null,"abstract":"<div><div>As manufacturing scales up and demand for customization grows, computer-aided inspection technologies face increasing pressure to enhance efficiency and capability. Inspection path planning (IPP) for touch-trigger probes on coordinate measuring machines (CMMs) still suffers from heavy reliance on manual rules and poor coordination among planning stages. To address these limitations, this paper proposes a novel approach for IPP based on a hybrid planning framework, called <strong>Inspection Path Optimization via Deep Reinforcement Learning & Heuristic Search (InPO-DRLHS)</strong>. InPO-DRLHS leverages hierarchical reinforcement learning (HRL) to generate collision-free paths (CFPs) while accounting for probe orientation changes, and integrates this architecture with heuristic-based optimization of measurement sequence to improve global planning efficiency. Specifically, within the HRL module: the lower-level agent employs an improved deep Q-learning approach to generate optimal CFPs under fixed probe orientations; the higher-level agent predicts optimal probe rotation positions from a voxelized scene representation, guiding orientation adjustments for the lower-level planning. Furthermore, the value function learned by the HRL module serves as a surrogate heuristic estimate within the heuristic algorithm during sequence planning, enabling path quality assessment without explicit CFP generation. Experimental results show that the proposed method achieves over 90% success rate in 3D CFP planning and reduces total measurement time by 21.7% compared to conventional approaches, indicating substantial gains in inspection efficiency.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 34-50"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928458","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}

引用次数: 0

Design of laser-assisted processing digital twin system 激光辅助加工数字孪生系统的设计

IF 14.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2026-04-01 Epub Date: 2026-01-13 DOI: 10.1016/j.jmsy.2026.01.001

Xuefeng Wu , Tiankuo Yu , Xianli Liu , Caixu Yue , Xulu Jiang , Jiayu Li , Zhonghua Li

{"title":"Design of laser-assisted processing digital twin system","authors":"Xuefeng Wu , Tiankuo Yu , Xianli Liu , Caixu Yue , Xulu Jiang , Jiayu Li , Zhonghua Li","doi":"10.1016/j.jmsy.2026.01.001","DOIUrl":"10.1016/j.jmsy.2026.01.001","url":null,"abstract":"<div><div>The machining of high-performance, difficult-to-cut materials poses a critical challenge in advanced manufacturing. While laser-assisted machining (LAM) has emerged as a viable solution, its effectiveness is often compromised in practice by insufficient synchronization between laser heating and cutting operations, leading to processing defects. To address this limitation, this study develops an intelligent collaborative LAM system based on digital twin technology. By integrating Unity3D with deep learning techniques, a systematic architecture suitable for laser-thermal-assisted machining is constructed. A dynamic multi-physics field coupling model is established to achieve real-time control of laser incidence posture along with simultaneous monitoring and prediction of the temperature distribution in the machining region. This integrated system exhibits enhanced laser positioning agility, reduced thermal fluctuations, improved laser energy utilization efficiency, and consistent processing quality. Experimental validation conducted on forged superalloy Inconel 718 and SiCp/Al composites demonstrates remarkable improvements in both surface integrity and dimensional accuracy. Moreover, machine learning-based reliability assessment reliability assessment confirms only minor deviations in experimental outcomes, thereby providing a robust intelligent process assurance mechanism for machining difficult-to-process components such as aero-engine blades.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 155-174"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978548","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}

引用次数: 0

Physics-informed machine learning across manufacturing processes: Recent advances, challenges, and directions 跨制造过程的物理信息机器学习：最新进展，挑战和方向

IF 14.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1016/j.jmsy.2026.01.002

Donghyun Ra , Jaeryun Lee , Minwoo Lee , Seongmin Kwak , Seungchul Lee , Sooyoung Lee

{"title":"Physics-informed machine learning across manufacturing processes: Recent advances, challenges, and directions","authors":"Donghyun Ra , Jaeryun Lee , Minwoo Lee , Seongmin Kwak , Seungchul Lee , Sooyoung Lee","doi":"10.1016/j.jmsy.2026.01.002","DOIUrl":"10.1016/j.jmsy.2026.01.002","url":null,"abstract":"<div><div>Data-driven methods have shown remarkable promise across manufacturing disciplines. However, their application to manufacturing processes, where physical behaviors dominate, faces several limitations including the need for large datasets, the risk of physically implausible results, and limited interpretability. Physics-informed machine learning (PIML), which incorporates governing physical laws into deep neural networks, has emerged as a promising direction for overcoming these limitations by enabling physically consistent and data-efficient modeling. In this article, we systematically analyze PIML applications from a manufacturing-process perspective. We organize the manufacturing landscape into five representative categories based on their dominant physical mechanisms and the availability of relevant literature: mechanical processes, chemical processes, thermal-driven processes, additive manufacturing, and semiconductor fabrication. We comprehensively review and discuss domain-dependent characteristics, including governing physics, data regimes, and modeling objectives across manufacturing processes. We further discuss fundamental challenges of PIML approaches, along with practical considerations for real-world deployment. This review aims to provide a foundation for accelerating physics-informed learning and its advancement within manufacturing research and practice.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 72-95"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979111","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}

引用次数: 0

Corrigendum to “ChatCNC: Conversational machine monitoring via large language model and real-time data retrieval augmented generation” [J Manuf Syst 79 (2025) 504–514] “ChatCNC：通过大型语言模型和实时数据检索增强生成的会话机器监控”的勘误表[J] Manuf system 79 (2025) 504-514]

IF 14.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2026-04-01 Epub Date: 2026-01-22 DOI: 10.1016/j.jmsy.2026.01.008

Jurim Jeon , Yuseop Sim , Hojun Lee , Changheon Han , Dongjun Yun , Eunseob Kim , Shreya Laxmi Nagendra , Martin B.G. Jun , Yangjin Kim , Sang Won Lee , Jiho Lee

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Large language models in human-robot collaboration: A systematic review, trends, and challenges 人机协作中的大型语言模型：系统回顾、趋势和挑战

IF 14.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2026-04-01 Epub Date: 2026-01-22 DOI: 10.1016/j.jmsy.2026.01.011

Gang Yuan , Xiaojun Liu , Maohua Xiao , Jinhua Xiao , Lihui Wang

{"title":"Large language models in human-robot collaboration: A systematic review, trends, and challenges","authors":"Gang Yuan , Xiaojun Liu , Maohua Xiao , Jinhua Xiao , Lihui Wang","doi":"10.1016/j.jmsy.2026.01.011","DOIUrl":"10.1016/j.jmsy.2026.01.011","url":null,"abstract":"<div><div>The integration of large language models (LLMs) into human-robot collaboration (HRC) represents a paradigm shift toward cognitive manufacturing under Industry 5.0. This systematic review analyzes 1278 publications to elucidate the enabling mechanisms, application hierarchies, and persistent challenges of LLM-enhanced HRC. We identify three agent paradigms, each tailored to structured, interactive, and sensor-rich scenarios, respectively. Furthermore, we propose a layered training framework combining domain-adaptive pre-training and scenario-specific fine-tuning to bridge general LLMs capabilities with industrial demands. Our findings reveal that LLMs significantly advance HRC in environmental perception, long-horizon task planning, and embodied interaction, yet face critical challenges in dynamic adaptability, multimodal fusion, and value alignment. This study provides a structured reference for developing robust, interpretable, and human-centric collaborative systems, outlining five key research directions to guide future advancements in cognitive manufacturing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 249-268"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035033","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}

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Predicting aircraft assembly gaps considering structural deformation: A CGAN-based surrogate modeling approach 考虑结构变形的飞机装配间隙预测：基于cgan的代理建模方法

IF 14.2 1区工程技术

Journal of Manufacturing Systems Pub Date : 2026-02-01 Epub Date: 2025-12-24 DOI: 10.1016/j.jmsy.2025.12.018

Haorui Sun , Yifan Zhang , Linbei Jiang , Shouguo Zheng , Qing Wang , Yinglin Ke

{"title":"Predicting aircraft assembly gaps considering structural deformation: A CGAN-based surrogate modeling approach","authors":"Haorui Sun , Yifan Zhang , Linbei Jiang , Shouguo Zheng , Qing Wang , Yinglin Ke","doi":"10.1016/j.jmsy.2025.12.018","DOIUrl":"10.1016/j.jmsy.2025.12.018","url":null,"abstract":"<div><div>The precise assembly of aircraft structures remains a critical challenge in aerospace manufacturing, as assembly gaps are a primary factor undermining precision. This study proposes a surrogate model for the rapid and accurate prediction of assembly gaps in the presence of structural deformation. The methodology begins with creating a comprehensive assembly gap dataset that integrates both geometric deviations and structural deformations through assembly modeling and automated workflow. Building upon this dataset, an enhanced PointNet+ + (PNP) network is employed to extract and fuse multi-source assembly features from part shape point clouds and tooling movements. These fused features are then integrated with a generation network based on the Conditional Generative Adversarial Network (CGAN) architecture, reformulating gap prediction as a conditional generation task. The innovative integration of the two networks realizes an end-to-end pipeline, from initial assembly feature extraction to final assembly gap prediction. A representative wing-box structure was employed as a case study to validate the approach. The trained model efficiently predicts gap fields directly from multi-source assembly information. Experimental results demonstrate that the proposed model achieves prediction accuracy comparable to virtual assembly methods while significantly enhancing computational efficiency. These findings underscore the model’s efficacy, positioning it as a valuable tool for rapidly predicting gaps in aircraft assembly.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 518-540"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836453","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}

引用次数: 0