IET Collaborative Intelligent Manufacturing最新文献

Fibre-reinforced composites manufacturing is a large and growing industry, with much of the work carried out manually by skilled human laminators. The physical nature of the work can be significantly deleterious to these workers' health, while growing demand requires increased rates of manufacture. Human–robot collaborative manufacturing offers a potential solution but requires the human to feel confident working with the robot and trust that they will be safe. Successful human trials of two different approaches to collaborative lay-up of fibre-reinforced plastic composites are presented, with tasks representative of manufacturing challenges in industry. Volunteer responses are measured by questionnaires, with users reporting the processes to be safe, simple to use and allowing greater ease of manufacturing than manual-only lay-up.

To expedite the modernisation of equipment construction and address practical challenges, such as low efficiency in armoured vehicle passenger information retrieval, diverse perception channels, and inadequate combat effectiveness in traditional vehicle-integrated electronic information systems, the authors aim to transition to a helmet-mounted display system (HMD). On the basis of the target mission stage of military vehicles, the authors have organised the required information items for the vehicle HMD, integrated the hierarchical relationships of interaction interface design elements, and formulated design strategies using the Garrett user experience element model. We have constructed a vehicle HMD interaction interface design model and conducted comparative experiments with typical vehicle electronic display system interfaces. The usability of the model has been verified through eye-tracking experiments and reaction time analysis. Experimental data indicates that the vehicle HMD interactive interface system, guided by the user experience element model, effectively enhances operational performance for passengers, demonstrating superior recognition, search ability, comprehensibility, and rationality. In conclusion, the vehicle HMD interaction interface design model, guided by the user experience element model, meets the requirements of vehicle HMD interaction interface design. It validates the effectiveness and feasibility of transitioning from a traditional vehicle-integrated electronic information system to a vehicle HMD, providing technical support for enhancing display efficiency in future prototype platforms on the prototype platform digital warfare.

A hybrid flow shop is pivotal in modern manufacturing systems, where various emergencies and disturbances occur within the smart manufacturing context. Efficiently solving the dynamic hybrid flow shop scheduling problem (HFSP), characterised by dynamic release times, uncertain job processing times, and flexible machine maintenance has become a significant research focus. A NeuroEvolution of Augmenting Topologies (NEAT) algorithm is proposed to minimise the maximum completion time. To improve the NEAT algorithm's efficiency and effectiveness, several features were integrated: a multi-agent system with autonomous interaction and centralised training to develop the parallel machine scheduling policy, a maintenance-related scheduling action for optimal maintenance decision learning, and a proactive scheduling action to avoid waiting for jobs at decision moments, thereby exploring a broader solution space. The performance of the trained NEAT model was experimentally compared with the Deep Q-Network (DQN) and five classical priority dispatching rules (PDRs) across various problem scales. The results show that the NEAT algorithm achieves better solutions and responds more quickly to dynamic changes than DQN and PDRs. Furthermore, generalisation test results demonstrate NEAT's rapid problem-solving ability on test instances different from the training set.

Distributed assembly permutation flow shop scheduling problem is the hot spot of distributed pipeline scheduling research; however, parallel assembly machines are often in the assembly stage. Therefore, we propose and study distributed parallel assembly permutation flow shop scheduling problem (DPAPFSP). This aims to enhance the efficiency of multi-factory collaborative production in a networked environment. Initially, a corresponding mathematical model was established. Then, an improved hybrid distribution estimation algorithm was proposed to minimize the makespan. The algorithm adopts a single-layer permutation encoding and decoding strategy based on the rule of the Earliest Finished Time. A local neighbourhood search based on critical paths is performed for the optimal solution using five types of neighborhood design. A dual sampling strategy based on repetition rates was introduced to ensure the diversity of the population in the later periods of iteration. Simulated annealing searching was applied to accelerate the decline of optimal value. Finally, we conduct simulation experiments using 900 arithmetic cases and compare the simulation experimental data of this algorithm with the other four existing algorithms. The analysis results demonstrate this improved algorithm is very effective and competitive in solving the considered DPAPFSP.

In industrial and real-world systems, recognising errors and adopting the best approaches are gaining relevance. The authors’ goal is to identify artificial intelligence apps that provide the most reliable and valuable data-based fault detection techniques. A system for fault identification is presented based on reinforcement learning and proximal policy optimisation (PPO). Due to the lack of fault data, one of the key issues with the standard policy is its inability to recognise fault classes; this issue was resolved by modifying the cost equation. Using improved PPO, the authors can improve performance, address data imbalances, and forecast possible failures more accurately. The approach utilises policy-based optimisation, which offers several advantages. Firstly, it directly optimises the advantage quantity, and secondly, it ensures the stability of function approximation. The authors have studied two different turbines in Iran and collected data from them separately when a fault occurred. To demonstrate the efficiency of our algorithm, the authors have included the third and fourth datasets as cyber attack benchmarks. When the authors’ proposed policy is adopted, all evaluation metrics will improve by 3%–4% as compared to the previous policy in the first benchmark, between 20% and 55% in the second benchmark, between 6% and 14% in the third benchmark, and between 4% and 5% in the fourth benchmark, with improved results and prediction times compared to existing studies.

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.