IET Collaborative Intelligent Manufacturing最新文献

RETRACTION: C. Ding, R. Kohli: Analysis of a building collaborative platform for Industry 4.0 based on Building Information Modelling technology. IET Collaborative Intelligent Manufacturing 3, no. 3, 233–242 (2021). https://doi.org/10.1049/cim2.12036.

The above article, published online on 21 August 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley & Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Furthermore, the manuscript contains multiple inconsistencies and several scientific statements are not supported by relevant references. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed of the decision to retract.

RETRACTION: P. R. Gabani, U. B. Gala, V. S. Narwane, R. D. Raut, U. H. Govindarajan, B. E. Narkhede: A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India. IET Collaborative Intelligent Manufacturing 3, no. 3, 262–272 (2021). https://doi.org/10.1049/cim2.12006.

The above article, published online on 16 February 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley & Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Furthermore, the conclusions of this manuscript are unsupported by any relevant experiments or calculations. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed of the decision and disagree with the retraction.

RETRACTION: H. Qiang, M.A. Ikbal, S. Khanna: Prediction of energy consumption of numerical control machine tools and analysis of key energy-saving technologies. IET Collaborative Intelligent Manufacturing 3, no. 3, 215–223 (2021). https://doi.org/10.1049/cim2.12001.

The above article, published online on 1 February 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.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley & Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the corresponding special issue underwent systematic manipulation. Furthermore, the manuscript contains various logical flaws as well as unrelated references that do not support the scientific statements made. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed of the decision to retract.

The development of multimodal large models and digital twin technology is set to revolutionise the methods of intelligent monitoring and maintenance for transformers. To address the issues of low intelligence level, single application mode, and poor human–machine collaboration in traditional transformer monitoring and maintenance methods, an intelligent monitoring and maintenance digital twin multimodal expert reasoning system, fine-tuned on visual language-based large models, is proposed. The paper explores the modes and methods for implementing intelligent monitoring and maintenance of transformers based on multimodal data, large models, and digital twin technology. A multimodal language large model (MLLM) framework for intelligent transformer maintenance, grounded on the Large Language and Vision Assistant model, has been designed. To enable large models to understand and reason about image annotation areas, an adaptive grid-based positional information processor has been designed. To facilitate the compatibility and learning of large models with transformer Dissolved Gas Analysis data, a heterogeneous modality converter based on the Gram–Schmidt angular field has been developed. For the unified modelling and management of multimodal reasoning and comprehensive resource integration in human–machine dialogue, a central linker based on an identity resolution asset management shell has been designed. Subsequently, a visual-language multimodal dataset for transformer monitoring and maintenance was constructed. Finally, by fine-tuning parameters, a multimodal expert reasoning system for intelligent transformer monitoring and maintenance was developed. This system not only achieves real-time monitoring of the transformer's operational status but also generates maintenance strategies intelligently based on operational conditions. The expert system possesses robust human–machine dialogue capabilities and reasoning generation abilities. This research provides a reference for the deep integration of MLLM and digital twin in industrial scenarios, particularly in the application modes of intelligent operation and maintenance for transformers.

RETRACTION: H. Sun, M. Fan, A. Sharma: Design and implementation of construction prediction and management platform based on building information modelling and three-dimensional simulation technology in Industry 4.0. IET Collaborative Intelligent Manufacturing 3, no. 3, 224–232 (2021). https://doi.org/10.1049/cim2.12019.

The above article, published online on 21st 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 and Sons Ltd.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley and Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. In addition, multiple inconsistencies and textual disconnections were found. As such, the research described is not comprehensible for readers. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed and they disagree with the retraction.

Numerous files, such as records and logs, are generated in the process of equipment diagnosis and maintenance (D&M). These files contain lots of unstructured plain text. Knowledge in these files could be reused for similar equipment faults. In practice, knowledge presented in plain text is hard to acquire. Thus, automated named entity recognition (NER) and relation extraction (RE) methods based on pretrained encoders could be used to extract entities and relations and develop a structured knowledge graph (KG), thus facilitating intelligent manufacturing. However, equipment fault NER exhibits suboptimal performance with existing encoders pretrained on general-domain corpus. In this paper, domain-adaptation-based NER with information enrichment is proposed for developing an equipment fault KG. A domain-adapted encoder is tailored for equipment fault NER through domain-adaptive pretraining (DAPT). Update of word segmentation dictionary and adjustment of masking approach are implemented during DAPT for information enrichment, which helps make the most of the limited domain-specific pretraining corpus. Experimental results show that the F1 score of NER is improved by 1.22% using the domain-adapted encoder compared to its counterpart using the encoder pretrained on general-domain corpus. Furthermore, a reliable and robust question answering (QA) application of the developed equipment fault KG is also shown.

Robot perception is an important topic in artificial intelligence field, and tactile recognition in particular is indispensable for human–computer interaction. Efficiently classifying data obtained by touch sensors has long been an issue. In recent years, spiking neural networks (SNNs) have been widely used in tactile data categorisation due to their temporal information processing benefits, low power consumption, and high biological dependability. However, traditional SNN classification methods often encounter under-convergence when using membrane potential representation, decreasing their classification accuracy. Meanwhile, due to the time-discrete nature of SNN models, classification requires a significant time overhead, which restricts their real-time tactile sensing application potential. Considering these concerns, the authors propose a faster and more accurate SNN tactile classification approach using improved membrane potential representation. This method effectively overcomes model convergence problems by optimising the membrane potential expression and the relationship between the loss function and network parameters while significantly reducing the time overhead and enhancing the classification accuracy and robustness of the model. The experimental results show that the propose approach improves the classification accuracy by 4.16% and 2.71% and reduces the overall time by 8.00% and 8.14% on the EvTouch-Containers dataset and EvTouch-Objects dataset, respectively, when compared with existing models.

A novel algorithm combining bidirectional recurrent neural networks (BiRNNs) with temporal difference is proposed for multi-objective energy-efficient non-permutation flow-shop scheduling problem (NFSP). The objectives of this problem involve minimising both the makespan and total energy consumption. To begin, a mathematical model is formulated to represent the energy-efficient NFSP. Subsequently, the NFSP is transformed into a Markov decision process, where an action space comprising 28 scheduling rules is constructed. Considering the global and local features of NFSP, a set of 15 state features is extracted. Different reward functions are then defined to correspond to the specific objectives. Furthermore, the state features of NFSP are extracted using a multi-layer perceptron model based on BiRNNs. By utilising the TD(λ) algorithm to calculate the state value function, various policies are generated. In order to evaluate the proposed algorithm, a new test set for the energy-efficient NFSP is constructed, building upon classic benchmark problems. Finally, comparison experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm.

This work focuses on detecting defects in welding seams using the most advanced You Only Look Once (YOLO) algorithms and transfer learning. To this end, the authors prepared a small dataset of images using manual welding and compared the performance of the YOLO v5, v6, v7, and v8 methods after two-step training. Key findings reveal that YOLOv7 demonstrates superior performance, suggesting its potential as a valuable tool in automated welding quality control. The authors’ research underscores the importance of model selection. It lays the groundwork for future exploration in larger datasets and varied welding scenarios, potentially contributing to defect detection practices in manufacturing industries. The dataset and the code repository links are also provided to support our findings.

To adapt to the dynamic, diverse, and personalised needs of customers, manufacturing enterprises face the challenge of continuously adjusting their resource structure. This has led manufacturers to shift towards a smart cloud manufacturing mode in order to build highly flexible production logistics (PL) systems. In these systems, the optimal configuring of PL resources is fundamental for daily logistics planning and vehicle scheduling control, providing necessary resources for the entire PL segment. However, traditional resource configuration methods face limitations, such as incomplete information acquisition, slow response in resource configuration, and suboptimal configuration results, leading to high subsequent operational costs and inefficient logistics transportation. These issues limit the performance of the PL system. To address these challenges, the authors propose a digital twin-based optimisation model and method for smart cloud PL resources. The approach begins with constructing an optimisation model for the PL system considering the quality of service for a cloud resource is constructed, aiming to minimise the number of logistics vehicles and the total cost of the PL system. Additionally, a DT-based decision framework for optimising smart cloud PL resources is proposed. Alongside a DT-based dynamic configuration strategy for smart cloud PL resources is designed. By developing a multi-teacher grouping teaching strategy and a cross-learning strategy, the teaching and learning strategies of the standard teaching-learning-based optimisation algorithm are improved. Finally, numerical simulation experiments were conducted on the logistics transportation process of a cooperating enterprise, verifying the feasibility and effectiveness of the proposed algorithms and strategies. The findings of this study provide valuable references for the management of PL resources and algorithm design in advanced manufacturing modes.