IET Collaborative Intelligent Manufacturing最新文献

Superalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro-Fuzzy Inference System for forecasting WEDM parameters using grey-based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision-making in the production of high-quality biomedical devices and components.

Model-based systems engineering is now leading the way in supporting the design of complex products or systems. The integration of modelling and simulation of continuous-discrete hybrid systems is the key of model-based systems engineering. But the existing languages, formalisms and tools cannot support the unified modelling and simulation of hybrid systems and therefore reduces the efficiency of complex system development. To address this issue, this paper develops a design method of complex hybrid systems, which integrates modelling and simulation of the continuous-discrete hybrid behaviour. Specifically, the authors provided a modelling method of hybrid systems based on the X language, a simulation method based on XDEVS, and a compilation algorithm to transform the hybrid model constructed with X language into XDEVS simulation files. In this way, the X language hybrid model can be automatically translated into XDEVS simulation files by a compiler. The simulation files can then be simulated by the XDEVS simulation engine. The obtained simulation results will be used to verify whether the design scheme meets the design requirements of the hybrid system. Finally, the correctness and feasibility of the proposed method are verified using a car-driving model.

RETRACTION: X. Wang, X. Zhang, H. Gong, J. Jiang, H. M. Rai: A flight control method for unmanned aerial vehicles based on vibration suppression. IET Collaborative Intelligent Manufacturing 3, no. 3, 252–261 (2021). https://doi.org/10.1049/cim2.12027.

The above article, published online on 26 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.

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. In addition, the manuscript contains flaws and inconsistencies. 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: Y. Ma, Q. Chen, S. Wang, S. Sharma, S. Khanna: Research on dispersion compensation using avalanche photodiode and pin photodiode. IET Collaborative Intelligent Manufacturing 3, no. 3, 205–214 (2021). https://doi.org/10.1049/cim2.12000.

The above article, published online on 17 December 2020 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, mistakes and inconsistencies were found in different figures of this manuscript. 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: Y. Xiao, Z. Jun, H. Lei, A. Sharma, A. Sharma: A novel method of material demand forecasting for power supply chains in industrial applications. IET Collaborative Intelligent Manufacturing 3, no. 3, 273–280 (2021). https://doi.org/10.1049/cim2.12007.

The above article, published online on 21 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. In addition, most graphs are missing relevant units and descriptors so that the results are not comprehensible. 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.

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.