Journal of Manufacturing Systems最新文献

{"title":"A dual-arm robotic system for automated multi-branch wire harness assembly in automotive industry","authors":"Pablo Malvido Fresnillo ,&nbsp;Saigopal Vasudevan ,&nbsp;Wael M. Mohammed ,&nbsp;Jose A. Perez Garcia ,&nbsp;Jose L. Martinez Lastra","doi":"10.1016/j.jmsy.2025.10.008","DOIUrl":"10.1016/j.jmsy.2025.10.008","url":null,"abstract":"<div><div>Wire harnesses are critical components in modern vehicles, responsible for transmitting electrical signals and power to sensors and actuators. Despite the high level of automation in the automotive industry, wire harness manufacturing still relies heavilylargely depends on manual assembly. This is due to the significant challenges posed by the process, such as the complexity of perceiving and manipulating flexible materials and the high degree of customization required. As a result, existing solutions only address specific assembly tasks, rather than the entire processare fragmented, unable to scale to full production, and remain economically unviable for high-mix scenarios. To bridge this gap, this paper presents a novel robotic system for fully automating wire harness assembly. The system adopts a task-level programming methodology that leverages process knowledge to enable fast and easy reconfiguration. Additionally, it incorporates specific solutions to address key challenges in multi-branch wire harness manipulation, such as cable separation and entanglement prevention. The system’s performance was evaluated in two real-world assembly scenarios using a dual-arm robot. Experimental results demonstrate the system’s effectiveness and ease of reconfiguration, achieving success rates of 55% and 73% in two complex multi-branch wire harness assembly processes, and highlight areas of improvement, which will be further investigated in future works. The system repository is openly available allowing other researchers to build their solutions upon the proposed methodology.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 577-596"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416704","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":"Uncertainty-aware power consumption prediction in customized stainless-steel manufacturing: A comparative study of hierarchical Bayesian and deep neural models","authors":"Akarawint Chawalitanont,&nbsp;Atit Bashyal,&nbsp;Hendro Wicaksono","doi":"10.1016/j.jmsy.2025.10.010","DOIUrl":"10.1016/j.jmsy.2025.10.010","url":null,"abstract":"<div><div>Energy-efficient and data-driven decision-making has become a critical priority in modern manufacturing, particularly in customized or make-to-order (MTO) production where product variability causes large fluctuations in power consumption. Existing prediction models in this domain are often deterministic, lacking the ability to quantify uncertainty and capture hierarchical data dependencies, which limits their reliability for operational use. This study addresses this gap by developing a hierarchical Bayesian learning framework for power consumption prediction in customized stainless-steel manufacturing. The objective is to design models that not only achieve high predictive accuracy but also provide calibrated uncertainty estimates to support risk-aware production decisions. Four models, i.e., Hierarchical Bayesian Linear Regression (HBLR), Hierarchical Bayesian Neural Network (HBNN), Fully Connected Neural Network (FCN), and One-Dimensional Convolutional Neural Network (1D-CNN), were implemented and benchmarked using three inference algorithms: No-U-Turn Sampler (NUTS), Automatic Differentiation Variational Inference (ADVI), and Stein Variational Gradient Descent (SVGD). The innovation lies in systematically quantifying uncertainty using coverage probability, sharpness, and calibration error, and in establishing a unified comparison between probabilistic and deterministic models. Results show that the HBLR–NUTS model achieves the best trade-off between accuracy (RMSE = 11.85) and calibration quality (coverage <span><math><mo>≈</mo></math></span> 0.98), while ADVI offers near-equivalent performance with significantly lower computation time. These uncertainty-aware predictions can be directly integrated into Manufacturing Execution System (MES) and Enterprise Resource Planning (ERP) environments for energy-optimized scheduling and cost-aware planning. The proposed framework provides a scalable, interpretable, and statistically reliable foundation for advancing sustainable, data-driven manufacturing analytics.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 713-735"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465401","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":"Towards a next-generation LLM empowered low-code programming industrial robotic system for human-centric smart manufacturing","authors":"Wenhang Dong ,&nbsp;Dongpeng Li ,&nbsp;Yuchen Ji ,&nbsp;Hongpeng Chen ,&nbsp;Shimin Liu ,&nbsp;Zheng Ma ,&nbsp;Fang Hao ,&nbsp;Yuqi Ji ,&nbsp;Hongwen Xing ,&nbsp;Pai Zheng","doi":"10.1016/j.jmsy.2025.10.012","DOIUrl":"10.1016/j.jmsy.2025.10.012","url":null,"abstract":"<div><div>Industrial robotic systems have been widely adopted in modern industries due to their advantages in high flexibility and strong adaptability. However, these systems are often limited by fragmented workflows, high cognitive demands on operators, and complex interaction programming. To address these issues, this study proposes a next-generation low-code programming framework empowered by large language models (LLMs), aiming to advance human-centric smart manufacturing (HCSM). By integrating the reasoning capabilities of LLMs into industrial robotic systems, the framework prioritizes intuitive, efficient, and operator-friendly interaction, establishing a novel paradigm for industrial applications. Additionally, the system incorporates a cognitive assistance module to reduce the cognitive burden on unskilled operators. Moreover, an LLM-based low-code programming module was designed, employing a multi-agent mechanism for intent recognition, parameter extraction, and human verification, thereby significantly enhancing the system’s ability to robustly handle unstructured natural language instructions in industrial environments. Finally, the system was validated through a case study on aircraft panel drilling, demonstrating its practicality and reliability while supporting unskilled operators in performing complex tasks. This validation indicates that the proposed method has broad potential for industrial applications.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 675-686"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465404","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":"Causal inference-based dynamic optimization for disassembly line balancing with uncertain component states","authors":"Yilin Fang,&nbsp;Zhiyao Li,&nbsp;Kai Huang,&nbsp;Junyufeng Chen,&nbsp;Ling Gui,&nbsp;Xinyi Chen","doi":"10.1016/j.jmsy.2025.09.018","DOIUrl":"10.1016/j.jmsy.2025.09.018","url":null,"abstract":"<div><div>Efficient resource recovery from end-of-life (EOL) products requires well-structured disassembly processes. A disassembly line with multiple workstations provides a systematic framework, assigning specific tasks to each station. The disassembly line balancing problem (DLBP) aims to optimize task allocation to improve performance indicators such as cycle time. However, in practical scenarios, the states of EOL product components are often uncertain during the disassembly process. To tackle this challenge, we propose a DLBP with uncertain component states (DLB-UCS) model, which incorporates component state changes as dynamic factors during EOL product disassembly. Unlike conventional DLBP models, DLB-UCS supports real-time adaptation to uncertain changes, making it more consistent with industrial conditions. To solve this problem, we develop a causal inference-based dynamic multi-objective evolutionary algorithm (CI-DMOEA) that simultaneously minimizes the total disassembly cycle time and the number of robotic units required. In particular, a causal feature selection technique based on conditional independence testing is used for efficient initial population generation, enhancing adaptability in dynamic environments. Extensive comparative experiments on eight disassembly scenarios against three state-of-the-art dynamic multi-objective optimization algorithms show that the proposed CI-DMOEA demonstrates superior performance and responsiveness.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 470-482"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266688","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 flexible tooling system for aero-engine pipelines with complex components based on human-robot collaboration","authors":"Bo Zhou ,&nbsp;Jibin Zhao ,&nbsp;Renbo Xia ,&nbsp;Yueling Chen ,&nbsp;Tianyu Zhang ,&nbsp;Hongfeng Wang ,&nbsp;Junwei Wang ,&nbsp;Jiangyu Li ,&nbsp;Jun Zhang ,&nbsp;Ming Li ,&nbsp;Yong Qiao","doi":"10.1016/j.jmsy.2025.09.005","DOIUrl":"10.1016/j.jmsy.2025.09.005","url":null,"abstract":"<div><div>The demand for the manufacturing multi-variety, small batch, and customized pipelines in the aero-engine manufacturing industry is continuously increasing. Driven by advanced manufacturing technologies, the key factor affecting product quality has shifted from various manufacturing stages to the final assembly stage. Currently, automated assembly solutions are constrained by technical bottlenecks and cost pressures, making them difficult to implement; as a result, assembly tasks still rely heavily on manual operations. To adapt to fierce market competition and production changes, manufacturers must strive to automate pipeline assembly. For this purpose, this paper proposes an automated flexible tooling system for pre-welding positioning and assembly quality evaluation. The system adopts a multiple collaborative robots’ architecture and performs reconfigurable assembly in a human-robot collaboration (HRC) mode. The specific implementation steps are as follows: first, workers install connectors on the fixtures of each robot's end effector, adjust them to an appropriate posture, and obtain the spatial posture data of the connectors through scanning; subsequently, a method for determining registration schemes is developed on the basis of the structural characteristics of the connectors. The feature elements are further extracted, and data registration is completed through human-computer interaction (HCI); then, an improved NSGA-III method that integrates the Levenshtein Distance Congestion Elimination (LCE) method is proposed. This method incorporates three types of constraints: obstacle avoidance constraints for the collaborative movement of multiple collaborative robots, feasibility constraints for robot movement, and rotational angle constraints for connectors. It solves the multi-objective optimization problem among total assembly time, uniformity of time allocation, and energy consumption, enabling rapid and efficient robot posture reconstruction; finally, simulation and experimental verification of the system are conducted. The field assembly verification results demonstrate that the assembly quality is significantly improved compared with that of traditional and recent representative algorithms. The proposed assembly method has an accuracy ranging from 0.0452 mm to 0.0807 mm, with an assembly precision of approximately 0.0607 mm, and ensures the stability and predictability of the assembly quality.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 103-125"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027193","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":"Probabilistic state–space modeling for robust condition monitoring of industrial equipment","authors":"Victor Vantilborgh,&nbsp;Tom Lefebvre,&nbsp;Guillaume Crevecoeur","doi":"10.1016/j.jmsy.2025.09.016","DOIUrl":"10.1016/j.jmsy.2025.09.016","url":null,"abstract":"<div><div>This paper proposes a generic low-cost method for probabilistic condition monitoring of industrial equipment. A computationally efficient recursive data-driven model is constructed that correlates real-time machine measurements with a quantity or several quantities of interest (QoIs), including artificial metrics such as the Remaining Useful Lifetime (RUL). To that end, a probabilistic state–space model (PSSM) is identified, based on a fully instrumented measurement set obtained from a limited set of experiments that can only be obtained in a specialized testing environment. The dataset contains both cheaply available sensory information as well as prohibitively expensive, invasive or artificially constructed sensory signals. To identify a Maximum Likelihood Estimate of the PSSM, we rely on the Expectation–Maximization (EM) algorithm and Sequential Monte Carlo (SMC) estimation techniques. During operation, only the vital, non-intrusive and cheap sensors are used. The PSSM is then repurposed to reconstruct the costly sensory signals, realizing an effective and general purpose virtual sensor. Our methodology demonstrates the capacity to robustly estimate unmeasured physical variables in real-time and artificially constructed prognostic QoIs, such as the RUL, even when working with an incomplete measurement array. We validate the presented methodology for condition monitoring on the C-MAPSS dataset and a solenoid valve (SV) use case. The presented tool has similar predictive capabilities as compared with other state-of-the-art RUL prognostic methods and furthermore provides uncertainty quantification and contextual information with respect to equipment health.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 505-516"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324717","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 interpretable industrial digital platform based on Dempster-Shafer theory for pre-diagnosis the quality of hot-rolled strip","authors":"Yu Liu ,&nbsp;Wen Peng ,&nbsp;Xuemeng Li ,&nbsp;Wenteng Wu ,&nbsp;Bingquan Zhu ,&nbsp;Xudong Li ,&nbsp;Yafeng Ji ,&nbsp;Dianhua Zhang ,&nbsp;Jie Sun","doi":"10.1016/j.jmsy.2025.11.008","DOIUrl":"10.1016/j.jmsy.2025.11.008","url":null,"abstract":"<div><div>Strip crown is a critical quality indicator in hot-rolled strip, as inaccurate prediction often leads to downstream flatness defects and reduced production efficiency. Traditional physical models and individual machine learning (ML) algorithms struggle to achieve stable and reliable performance under the noisy and imbalanced conditions of industrial production. To address this challenge, this study proposes an adaptive hybrid prediction framework (A-HPF) based on Dempster-Shafer evidence theory. The framework integrates multiple ML, incorporates the support vector machine synthetic minority over-sampling technique to mitigate class imbalance, and introduces an adaptive conflict resolution mechanism with fuzzy affiliation functions to dynamically optimize evidence fusion. To ensure transparency and trustworthiness, SHapley Additive exPlanations combined with a causal network is employed to interpret the A-HPF’s decision logic and trace potential root causes. Validated on a 2160 mm hot rolling line, the proposed method improves classification accuracy by 5.9–15.6 %, reduces misclassification in edge categories by 63.6 %, and achieves an overall accuracy exceeding 0.93 across eight major steel grades. These improvements are further embedded into an industrial digital platform, enabling real-time quality prediction, interpretable analysis, and decision support, providing a practical solution for intelligent quality management in hot strip rolling.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 878-903"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516967","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":"Cyber-physical internet enabled hierarchical attention network based reinforcement learning for order dispatch in fast fashion manufacturing","authors":"Yanying Wang ,&nbsp;Zhiheng Zhao ,&nbsp;Yujie Han ,&nbsp;Ying Cheng ,&nbsp;George Q. Huang","doi":"10.1016/j.jmsy.2025.10.014","DOIUrl":"10.1016/j.jmsy.2025.10.014","url":null,"abstract":"<div><div>Fast fashion platforms such as SHEIN coordinate thousands of small, nearby garment factories to fulfil large numbers of small-lot, fast-switch orders. Dispatching each order operation to the most suitable factory while respecting process routes creates a Flexible Job Shop Scheduling Problem with sequence-dependent setup times (FJSP-SDST), which is NP-hard and must be solved repeatedly as new batches arrive. Current end-to-end deep reinforcement learning (DRL) schedulers either reschedule every batch from scratch or disregard setup costs, which undermines efficiency. We introduce a Cyber-Physical Internet (CPI) scheduling framework that provides routers for every factory and the company, enabling them to cache solved schedules and broadcast real time factory states. This approach skips redundant computations and supplies fresh setup time data. Within this framework, we have developed a Hierarchical Attention Network based Reinforcement Learning (HANRL) scheduler to model the interactions between orders and factories, as well as factory competition and setup costs. Experiments on synthetic and public benchmarks demonstrate that HANRL reduces makespan and improves generalization over state-of-the-art DRL baselines, all while retaining sub-second decision times. This proves the suitability of HANRL for large scale social manufacturing environments.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 784-799"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516964","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":"Flexible pallet automation system scheduling with limited fixture-pallets and material-pallets: A case study from an engine manufacturing enterprise","authors":"Yulu Zhou ,&nbsp;Shichang Du ,&nbsp;Jun Lv ,&nbsp;Xiaoxiao Shen ,&nbsp;Andrea Matta ,&nbsp;Siyang Wang","doi":"10.1016/j.jmsy.2025.09.015","DOIUrl":"10.1016/j.jmsy.2025.09.015","url":null,"abstract":"<div><div>Pallet automation system (PAS) is crucial for enterprises to organize and schedule limited resources, such as fixture-pallets (FPs) and material-pallets (MPs). In customized production, FPs are often insufficient and unbalanced. To address this, MPs are prepared to store workpieces to release FPs' capacity. In this way, FPs are utilized for processing, while MPs are leveraged for storage. However, existing studies mainly focus on fixtures that are fixed to machines and rarely consider FPs and MPs. To address this gap, this paper investigates the flexible pallet automation system scheduling with limited FPs and MPs (FPASFM). Firstly, a mathematical model is established to minimize the makespan. Secondly, a five-layer encoding strategy, a new decoding method, and a feasibility correction strategy are integrated to obtain feasible solutions. Thirdly, an improved meta-heuristic algorithm with rule-based initialization and critical path mutation (IMHRC) is proposed. Finally, effective initialization rule combinations are identified through experiments with 36 different rule combinations. 15 real-data case studies show that IMHRC outperforms six other algorithms. Additionally, IMHRC significantly reduces makespan by 59.66 % and 45.90 % for two real orders, while enhancing resource utilization. IMHRC demonstrates the ability to obtain superior solutions in a shorter time, with its advantages in large-scale problems, effectively meeting the practical demands of enterprises in real-world production environments.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 357-371"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155325","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":"AI Agents and Agentic AI–navigating a plethora of concepts for future manufacturing","authors":"Yinwang Ren,&nbsp;Yangyang Liu,&nbsp;Tang Ji,&nbsp;Xun Xu","doi":"10.1016/j.jmsy.2025.08.017","DOIUrl":"10.1016/j.jmsy.2025.08.017","url":null,"abstract":"<div><div>AI agents are autonomous systems designed to perceive, reason, and act within dynamic environments. With the rapid advancements in generative AI (GenAI), large language models (LLMs) and multimodal large language models (MLLMs) have significantly improved AI agents’ capabilities in semantic comprehension, complex reasoning, and autonomous decision-making. At the same time, the rise of Agentic AI highlights adaptability and goal-directed autonomy in dynamic and complex environments. LLMs-based AI Agents (LLM-Agents), MLLMs-based AI Agents (MLLM-Agents), and Agentic AI contribute to expanding AI’s capabilities in information processing, environmental perception, and autonomous decision-making, opening new avenues for smart manufacturing. However, the definitions, capability boundaries, and practical applications of these emerging AI paradigms in smart manufacturing remain unclear. To address this gap, this study systematically reviews the evolution of AI and AI agent technologies, examines the core concepts and technological advancements of LLM-Agents, MLLM-Agents, and Agentic AI, and explores their potential applications in and integration into manufacturing, along with the potential challenges they may face.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 126-133"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046929","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}