IET Collaborative Intelligent Manufacturing最新文献

With the continuous upgrading and transformation of the intelligentisation of China's manufacturing industry, and in response to the requirements for further intelligentisation of the phosphor copper ball production line proposed by a new electronic material company, this study proposes a fault prediction and diagnosis method based on big data. A high-efficiency distributed big data platform is constructed, and a workshop-level monitoring centre with the Windows control centre (WinCC) as the core is formed. The WinCC configuration software is used to monitor the key parameters of the equipment during the operation phase, and the login interface is configured according to the requirements of workshop information integration, for example, display interface, alarm interface, debugging interface, trend graph and other common functions. Cloud platforms and virtual private network (VPN) communication are used to realise remote maintenance. Aiming at the common fault problems in the production process, an expert diagnosis system based on fault tree analysis is constructed by fusing the fault tree theory and expert systems. The fault tree model of the unqualified phosphor copper ball production quality and the failure of the hydraulic system is highlighted. Therefore, ensuring the safety of the phosphor copper ball production line is of great significance to the entire production system.

Remote control for the position and status of a machine or an equipment can often be teleoperated by gestures in an intelligent manufacturing environment. In order to solve the problems that gestures with two directions such as left and right cannot be detected by single ultrasonic frequency, double different ultrasonic frequencies are used to detect gestures by the Doppler shift, and an algorithm of the recognition gesture based on the DAG-SVMs mixed Hidden Markov Model (HMM) is proposed to identify and classify the extracted feature sequences. Thus, four more types of gestures are expanded other than that of reading display screen information, and the comparative experiments to classify and recognise gestures of teleoperation are made with DAG-SVMs, the HMM, the DAG-SVMs mixed HMM, and other improved HMM algorithms. The test results have shown that the mean rate of gesture recognition for the algorithm based on the DAG-SVMs mixed HMM is 94.917%, which is 9.497% higher than that of the unimproved HMM, and its recognition accuracy of complex teleoperation gestures is improved by 2.3% compared with other improved HMM algorithms. The experimental results show that the DAG-SVMs mixed HMM algorithm has a good effect on recognition for the gestures of teleoperation and it can perform gesture recognition accurately.

Generally, scheduling problems refer to allocation of available shared resources and the sorting of production tasks, in order to satisfy the specified performance target within a certain time. The fundamental scheduling problem is that all jobs need to be processed on the same route, which is called flow shop scheduling problems (FSSP). The goal of FSSP, proven as an NP-hard problem, is to find a job sequence that minimizes the makespan. In this paper, an improved Q-learning algorithm is proposed for solving the FSSP. Firstly, a problem model based on the basic Q-learning algorithm is constructed. The makespan is used as the feedback signal, and the process of environmental state change is defined as the process of job selection. Q-learning gives the expected utility of taking a given action in a given state. Afterwards, combined with the NEH heuristic, the algorithm efficiency is enhanced by changing the job inserting mode. In order to validate the proposed method, several simulation experiments are carried out on a set of test problems having different scales. The obtained optimization results of the proposed algorithm are compared to the standard Q-learning algorithm and a hybrid algorithm. The discussion and analysis show that the proposed algorithm performs better than the others in solving the permutation FSSP. As a future direction, in order to shorten the running time, further improvements will be studied to increase the performance of the proposed algorithm and make it applicable and efficient for solving multi-objective optimization problems.

As digital technology and connectivity advance rapidly, the premise of bringing supply chain (SC) visibility across multiple tiers of supply, whilst facilitating the velocity to achieve strategic business objectives, is gaining interest. The feasibility and timing for successful adoption and implementation of such technology depend primarily on the readiness level and specific needs of each organisation, making it imperative to exercise insightful judgement as it can be expensive to acquire, develop and master. This research study examines the market pull versus technology push components of the functionalities enabled by digital SC control towers and buildings on the outcome of an extensive survey and expert interviews and proposes an assessment tool to aid decision making for the consideration of their adoption.

This survey provides a review of the fundamental approaches to design for mass customisation (DFMC), design for manufacturing (DFM) and design for supply chains (DFSC). The key term here is design while mass customisation, manufacturing and supply chain are the contexts from which the respective design objectives are derived. While these three areas of design are closely related, they have different focusses, which is reflected in the broader range of approaches proposed in the literature. The authors look at the literature through the lens of the product, process, and supply chain optimisation, with a variety of objectives ranging from improving product quality and variety while reducing costs, minimising environmental impacts and optimising supplier manufacture cooperation. In addition to the reviews of the approaches to DFMC, DFM and DFSC, recommendations on their practical system implementations are provided. While the authors acknowledge that the richness of the literature of each of the three design areas warrants a dedicated literature review, the main purpose of this survey is to pursue an integrated view on the three design issues faced by modern manufacturers and provide them and other related practitioners with a summary of representative approaches in the literature. Although it was not intended to conduct an exhaustive literature review of the literature, researchers from academia may still find the work useful by looking at the interactions of the three design areas from the perspective of joint-decision making, which is the angle from which the literature is approached.

In the modern age of Industry 4.0 and manufacturing servitisation, energy saving and environment consciousness are regarded as vital themes in manufacturing processes to reduce carbon tax and achieve sustainable development. For the past 20 years, the concept of green manufacturing has grown from infancy to a fully formed framework agreed upon by world-leading enterprises. With the unprecedented development of the information technology today, the industrial data collected could assist in the in-depth study on green manufacturing, which ranges from the operations of machining tools all the way to supply chain management. The wide scope of research promises a tremendous amount of annual publications in this field. To better facilitate follow-up research work, the present study provides a systematic overview of green manufacturing-related areas, including research progress and the developed features. The article set retrieved from the Web of Science contains 5989 documents related to green manufacturing. It is revealed that Journal of Cleaner Production is the most productive journal, archiving documents within the scope of green manufacturing. P. R. China tops the list of the number of documents with 1357 documents (22.66%), while Zhejiang University is the most productive institution. As the cooperation network indicates, P. R. China and the United States maintain the strongest collaborative links with other countries/regions. Finally, possible future directions are recommended based on the findings in the study. For instance, additive manufacturing technology and industrial IoT both have a great potential in green manufacturing; the weak link between the disciplines of manufacturing engineering and environmental science is expected to be strengthened, and a stronger international cooperation is believed to be beneficial to the field for the otherwise isolated countries/regions.

Technology and revolutions have powered the growth of Industry 4.0, the fourth industrial uprising. Industry 4.0 inspires progress and expansion through its efficiency capacity, as given in the literature. To improve the design quality and design efficiency of construction engineering units, this study adopts Building Information Modelling technology concepts. The implementation of Building Information Modelling in construction developments includes the digital demonstration of the fleshly and efficient features of the constituents that organise a production project. The centre for International Finance Corporation Standards' research is to achieve the goal of collaborative work in the whole life cycle of buildings and the Building Information Modelling technology building collaboration platform. Current challenges and limitations of the typical Building Information Modelling architectural collaboration platform based on the Building Information Modelling application are discussed in detail with some possible suggestions. The research shows that the development of a Building Information Modelling architectural collaboration platform based on the Building Information Modelling application software is very critical. The construction collaboration platform based on Building Information Modelling technology can achieve a higher degree of data sharing and strengthen the coordination of the work of various agencies in construction engineering.

Textile materials have been enriched in function at the composite level with continuous developments in the textile industry. Zinc oxide (ZnO) nanoparticles (ZnO-NPs) are strongly influenced by ultraviolet (UV) filter, antifungal, high catalysis, and semiconductor/piezoelectric coupling characteristics. Therefore, the antibacterial property and UV resistance of ZnO-NP materials are zcomprehensively analysed to provide a basis for applying ZnO-NP in the textile industry. In addition, the textile preparation and application of ZnO-NP in piezoelectric power generation is discussed. Based on relevant documents for ZnO-textile industry applications, scanning electron microscopy analysis, biological activity analysis, and UV transmittance analysis of textiles containing composite materials prove that textiles based on ZnO-based composite materials (ZnO-NP materials) have antibacterial properties and UV resistance. The antibacterial property and UV resistance of ZnO-NP materials are analysed comprehensively to provide a basis for applying ZnO-NP in the textile industry. After the photocatalytic reaction, its practical application as slurry type suspensions is limited because of the difficulty of separating the catalyst particles. In terms of its piezoelectric power generation characteristics, intensity of current voltage analysis and X-ray diffraction analysis reveal that textiles based on ZnO-NP materials have obvious semiconductor characteristic and obvious current enhancement signals locally, indicating that the textiles can achieve better piezoelectric properties.

Lithium-ion batteries have become a core component of electric vehicles (EVs) because of their high energy density. However, several issues in lithium-ion batteries usage, such as safety, durability, charging time, and driving range, limit the development of EVs. Meanwhile, with the emergence of Industry 4.0, the digital twins technology has received widespread attention in the manufacturing industry because it provides real-time monitoring and intelligent management of the production process. The authors propose a framework based on digital twins, which can be used for real-time monitoring, intelligent management, and autonomous control of battery packs. The framework covers all aspects of a battery pack's lifecycle, including design, manufacturing, operation monitoring, and second use options. Such a framework can solve some critical issues inhibiting the usage of batteries. A case study of the application of the proposed digital twins-based framework to electric vehicle battery systems has been conducted. The results show that deploying digital twins into the battery packs of EVs will improve the safety and service life of the battery packs.