IET Collaborative Intelligent Manufacturing最新文献

Large and complex workpieces are core components in fields, such as aerospace, shipbuilding, and other industrial applications. However, the main challenge of curved plate processing comes from the difficulty in determining the nonlinear rebound features with structural design parameters. An intelligent method framework is proposed for 3D surface manufacturing in cloud-edge collaboration environment. With the construction of an intelligent generation method for machining parameters, a unified data model is effectively integrated with various discrete data, and an intelligent processing mechanism based on 3D point clouds is constructed. In particular, a prediction method for curved panel rebound is constructed to reduce the manual dependency of the manufacturing process. Finally, a related case study is conducted to verify the framework, and the result shows accuracy, interpretability and reusability advantages over other similar methods.

The benefits of advancements in information and communication technologies have proliferated in manufacturing applications as more industries are migrating towards industry 4.0 compliance. The industry 4.0 process lines should be dynamic and reconfigurable. Digital twin (DT), supported by real-time data, is getting wide acceptance as a tool for monitoring and control of complex processes. Virtual commissioning (VC) has played a vital role in the software-based validation of the control systems. A DT-based VC methodology is proposed to evaluate and validate a reconfigured process line. The proposed new asset is commissioned virtually in the DT environment maintaining other stations and parameters synchronised. The proposed methodology is validated in a modular production system assembly line. A storage and retrieval station is virtually commissioned by the hardware in loop technique in the assembly line DT with a station time error of 1.3% between the VC model and the actual assembly line data. The case study demonstrates the feasibility of the proposed methodology in assessing the impacts due to reconfiguration of a process line. The findings offer significant support to decision makers in taking informed decisions and to reduce unforeseen interruptions resulting from the integration of a new asset with the existing process line.

RETRACTION: R. Huang, S. Zhang, W. Zhang, X. Yang, “Progress of Zinc Oxide-Based Nanocomposites in the Textile Industry,” IET Collaborative Intelligent Manufacturing 3, no. 3 (2021): 281–289. https://doi.org/10.1049/cim2.12029.

The above article, published online on 24 May 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

The retraction has been agreed on after concerns about this manuscript were raised by a third party. An investigation revealed several inconsistencies regarding the experiments described and the results presented. Furthermore, multiple references are unrelated to this manuscript and are considered insufficient to support the corresponding statements in the text. The experimental methods are not described in detail, and so the research is not comprehensible for the readers, the experiments are not reproducible, and the conclusions are considered invalid. The authors have been informed of the decision to retract.

RETRACTION: R. Huang, P. Yan, X. Yang, “Knowledge Map Visualization of Technology Hotspots and Development Trends in China's Textile Manufacturing Industry,” IET Collaborative Intelligent Manufacturing 3, no. 3 (2021): 243–251, https://doi.org/10.1049/cim2.12024.

The above article, published online on 27 March 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief, Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

The retraction has been agreed on after concerns about this manuscript were raised by a third party. An investigation revealed substantial flaws in the literature analysis presented. The methodical details are described insufficiently. Accordingly, the literature analysis results cannot be reproduced, and the conclusions are considered invalid.

The authors have been informed of the decision to retract.

Although the move to more service-oriented business can be beneficial even to smaller firms, servitization in SMEs remains a largely unexplored topic. The authors contribute to fill this gap exploring how SMEs can overcome the knowledge gaps of servitization faced by companies in the early-stages of this journey. By combining systematic literature review and expert panel methodology, the authors identify three knowledge gaps that hinder servitization initiatives in SMEs and propose a set of managerial recommendations to tackle with these gaps. In particular, the authors suggest a structured plan of recommendations, and point out how each stakeholder can contribute to fill the mentioned gaps. The proposed actions are specifically suggested for SMEs and focus on greater engagement of internal and external stakeholders. In addition to contributing to the domain scientific research on servitization, the authors therefore respond to the call for application-oriented research.

Agritech uses advanced technologies to boost the efficiency, sustainability, and productivity of farming. The Internet of Things (IoT) in agriculture has brought sensors and networked technology to gather and analyse environmental and crop data, enabling precision farming that optimises resource usage and increases yields. Yet, current agricultural methods suffer from unsecured and decentralised data management, causing inefficiencies and complicating traceability across the supply chain. The integration of IoT with blockchain technology is seen as a promising solution to enhance data-driven agriculture. Blockchain provides a secure, decentralised, and transparent ledger that enhances data integrity, reduces fraud, and improves traceability, which complements IoT applications. The authors detail the development of an innovative system that orchestrates IoT and blockchain technologies to facilitate the adoption of new technologies in agriculture and overcomes the lacked of comprehensive data connectivity. It outlines a conceptual framework and its preliminary empirical implementation. The system consists of three integrated layers: the IoT layer, which creates digital twins of field crops; the blockchain layer, which secures and manages data from the field and external stakeholders for dynamic applications such as track and tracing; and the orchestration layer, which fuses physical and digital data to optimise business models, enhance supply chain productivity, and support governmental policy-making, thereby improving field productivity and food sector innovation.

Nuclear power turbine fault diagnosis is an important issue in the field of nuclear power safety. The numerous state parameters in the operation and maintenance of nuclear power turbines are collected, forming a complex high-dimensional feature space. These high-dimensional feature spaces contain redundant information, which increases the training cost and reduces the recognition accuracy and efficiency of the fault diagnosis model. To address the aforementioned challenges, a vibration fault diagnosis algorithm in nuclear power turbines is proposed. First, a long short-term memory-based denoising autoencoder (LDAE) is designed to enhance the capability of uncertainty awareness. Then, a feature extraction method integrating variational mode decomposition (VMD), L-cliffs-based effective mode selection, and sample entropy is devised to extract the latent features from the complex high-dimensional feature space. Furthermore, using extreme gradient boosting (XGBoost) as the classifier, LDAE-VMD-XGBoost model is constructed for fault diagnosis of nuclear power turbines. Considering the impact of multiple hyperparameters of LDAE-VMD-XGBoost model on the performance, the pathfinder algorithm is used to optimise the model hyperparameter settings and improve the fault diagnosis accuracy. Experimental results demonstrate the performance of the proposed improved LDAE-VMD-XGBoost in accurate nuclear power turbine vibration fault diagnosis.

Decentralised federated learning (DFL) transforms collaborative energy consumption prediction using distributed computation across a large network of edge nodes, ensuring data confidentiality by eliminating central data aggregation. Preserving individual privacy in energy forecasting is paramount, as it safeguards personal data from unauthorised examination. This highlights the importance of effectively handling local data to provide privacy protection. The authors proposed a DFL framework for residential energy forecasting, focusing on improving the performance and convergence of the collaborative model. The proposed framework enables local training of the long short-term memory model with real-time household energy data in a ring topology. Importantly, the framework addresses the issue of straggler nodes, nodes that lag in computation or communication, by proposing a heuristic straggler identification and mitigation mechanism to reduce their negative impact on overall system performance and communication efficiency. This approach improves collaborative energy prediction performance and ensures an overall reduction in waiting time, thus improving the convergence performance. Experimental results consistently demonstrate a low mean absolute error ranging from 3 to 3.2 across all edge nodes. The empirical findings unequivocally illustrate the efficiency of the proposed DFL architecture, highlighting its ability to improve communication efficiency and concurrently enhance performance.

Moth-flame optimisation (MFO) algorithm has received a lot of attention recently, due to its simple structure and easy coding. Researchers have demonstrated that the original MFO algorithm suffers from the drawbacks of insufficient variety, slow convergence speed, and readily sliding into local optimum, which are brought about by the imbalance between local and global search. Reinforcement learning driven moth-flame optimisation (RLMFO) algorithm is designed to correct these issues. Opposition learning is employed to broaden the variety of the initial population. Reinforcement learning is introduced to direct the local and global search process of the algorithm. A strategy pool containing Gaussian mutation (GM), Cauchy mutation (CM), Lévy mutation (LM), and elite strategy (ES) is created to hold strategies with various functions. RLMFO is verified on the benchmark test suite in CEC 2017. RLMFO performs better than cutting-edge algorithms according to experimental findings.

Object detection techniques have been widely used in power system equipment maintenance. However, in power systems, the accuracy of object detection is limited by the scarcity of publicly available datasets and the lack of scene pertinence. In order to solve these problems, an object detection method based on cloud data fusion and transfer learning (YOLO-DFT) for power system equipment maintenance is proposed. Illustratively, YOLO-DFT focuses on the object detection task involving birds and humans, generating a substantial and resilient human-bird dataset through cloud-based data fusion to compensate for the dearth of public datasets in the power system domain. By seamlessly integrating the YOLOv5 algorithm with a transfer learning strategy, a targeted detection mechanism for specific locations is meticulously formulated. The experimental results demonstrate that YOLO-DFT effectively addresses object detection challenges in power systems, achieving a Mean Average Precision (MAP) measure of 0.925 across all classes, thereby providing a valuable reference for the maintenance of power system equipment.