IET Collaborative Intelligent Manufacturing最新文献

Block fabrication is the process that has the greatest impact on shipbuilding efficiency, so block spatial scheduling is widely studied as the key to improving shipbuilding efficiency. The shipbuilding spatial scheduling problem addresses the coupling characteristics of time and space. It is difficult to balance these two aspects. Based on the characteristics of spatial scheduling problems in shipbuilding enterprises, a three-dimensional space that uses time as the third dimension is imported, and a cellular automata model along with some evolutionary rules is built, which includes shape optimization rules, cluster or edge rule-based layout rules, and First Come First Service dispatching rules. The objectives are to achieve the minimum total completion time, the largest utilization of space and machine, and the least number of delay blocks. Taking the real data in a block workshop of a shipbuilding enterprise as an example, the feasibility and effectiveness of the algorithm are verified by comparing the statistical analysis with other algorithms.

In the field of industrial design and manufacture, computer-supported collaborative work (CSCW) systems have been widely deployed for better teamwork. However, the traditional CSCW systems have a main drawback in effectively processing and utilising knowledge across different industrial workflows. To bridge this gap, we propose a framework for collaboration between members across the manufacturing value chains to increase efficiency and reduce duplication in team cooperation. The framework contains three parts, namely workflow, knowledge mining, and services. Specifically, the workflow part provides a collaborative environment for multiple users. The knowledge mining part, as the core of the framework, extracts in-context knowledge from workflows. The part of services can interact with users with different users in each workflow, including information recommendation they need in the future or information retrieval they want to know from other workflows. Furthermore, we develop a prototype system for supporting multiple value chains collaboration to verify the effectiveness and efficiency of the framework.

To make charging of electric vehicles (EVs) more convenient, the service providers of charging stations (CSs) establish a large number of CSs. Existing methods address the problem of reducing costs and increasing revenue for the service providers from multiple aspects, such as CS location optimisation and charging pricing strategy. This study proposes multi-parameters-based-dynamic scheduling with energy management for the CSs, considering energy management and EV charging scheduling (EVCS). A fully functional battery management system is designed for energy storage. A multi-parameters optimisation algorithm is proposed by designing the CS selection operator based on alternative set and adjusting parameters. The experiments show that our proposed algorithms got better performance in terms of optimisation effect, the number of iterations, and stability.

Digitalisation creates new opportunities for businesses to implement and manage collaborative ecosystems both internally and externally. Digital twin (DT) is a rapidly emerging technology that can be used to facilitate new models of interaction and sharing of information. DT is the digital version of a physical process or asset that can be used to model, manage, and optimise its physical counterpart. Connecting multiple DTs is vital to provide a holistic integration and view across complex ecosystems. To create a DT-based collaborative ecosystem architecture, the following concerns need to be addressed. Trust is a fundamental requirement because multiple parties will work together as part of a composite DT. Interoperability is essential, as DTs from various domains will be required to interconnect and operate seamlessly. Finally, the governance is challenging as different scenarios require various mechanisms and governance structures. This study presents an architecture to enable multiple DT-based collaborative ecosystems, and example use case scenarios to demonstrate its applicability in collaborative manufacturing.

In the digital era, realising intelligent digital transformation is a major challenge in the manufacturing field. Digital transformation means bringing more profit appreciation. To improve the analysis reliability of value-added processes, this study proposes a method for assessing enterprises value-adding activities. For this purpose, a hybrid model is constructed based on data and mathematics, bridged by a server. The research builds an element group model that identifies data from different sources, and also gives a mathematical model to describe the relationship of the supply, marketing and service. Taking an automobile manufacturing value chain as an example, to theoretically analyse the composition of value-added activities. Then, the assembly process of an automobile manufacturing plant was used as a value-added case study. The simulation results show the impact of changing production layout and product handling angle on the whole value chain. The study can provide new ideas for the intelligent digital transformation of the manufacturing industry.

The occurrence of human errors significantly affects the performance and economic results of production systems. In this context, Human Reliability Analysis (HRA) methods play a key role in assessing the reliability of a man–machine system. Several HRA methods use Performance-Shaping Factors (PSFs), that is, all the aspects of human behaviour and environment that can affect human performance, to evaluate the Human Error Probability (HEP). However, despite the greater emphasis given by researchers to define of PSFs in recent years, the changes caused by the new enabling technologies implemented in manufacturing systems and derived from the Industry 4.0 paradigm have not yet been fully explored. Focussing on Human–Robot Collaboration (HRC) in production systems, the authors aim to define a PSF taxonomy that is useful for HEP evaluations in collaborative environments. To the best of the authors' knowledge, HRA approaches have not been investigated yet for HRC applications. The proposed taxonomy, which results from the integration of the most significant factors impacting workers' performance in HRC into the PSFs provided by an HRA method, can represent an important contribution for researchers and practitioners towards improving HRA methods and their applications in the context of Industry 4.0.

The field of Human-Robot Interaction (HRI) represents one of the fast-growing focus areas of Digital Twins (DTs). However, the role of DTs applications in human-robot collaborative systems is still uncertain. This review article provides a comprehensive perspective of DTs' critical design aspects (i.e. Objectives, associate technologies, and application scenarios) in the broad application areas of human-robot systems. This article uses a multi-faceted approach to comprehend 43 DTs' state-of-the-art applications in HRI. The study investigates the literature body across two dimensions (i.e. DT roles and HRI application characteristics). The conclusion of this work draws the attention of the relevant scientific community towards potential DTs' application scenarios and provides insights into DT's future research directions.

With the trend of supply chain globalisation, competition among enterprises is becoming more intense. Enterprises urgently need to improve their core competitiveness, and the enhancement of the competencies can depend on technologies services and the quality of suppliers. Since external factors are less controllable, this study starts with the quality of suppliers and proposes a supplier evaluation method that combines particle swarm optimisation with neural network algorithm to maximise the interests of enterprises. The particle swarm algorithm to lock the approximate location of the global optimum is first employed. Based on this, we establish an evaluation model of suppliers to train for the minimum errors between the desired and predicted values by constructing a back propagation (BP) neural network. Finally, the output results of the proposed method is compared with the BP neural network without the particle swarms optimisation. The proposed model is less empirically sensitive to the initialisation and can quickly converge to the local optimums, which overcomes the shortage of traditional neural networks and is more applicable to supplier evaluation.

Job shop scheduling problem (JSP) is a classical system resource optimisation problem and also an NP hard problem. The search algorithm based on Akers obstacle graph model is an effective algorithm to solve JSP, which first removes part of jobs from the original schedule, then constructs obstacle graph and finds the shortest path from the graph, and finally reinserts the jobs according to the shortest path decoding method to get the new schedule. Although the new scheduling can achieve good results, it is time-consuming to find the shortest path. Therefore, it is necessary to further study how to quickly plan the shortest path. This study presents a fast layered path search algorithm for solving the obstacle graph of job shop scheduling. The algorithm designs a node expansion method and a delay distance formula. The obstacles generated by different machines in the obstacle graph are layered. When the nodes expand, the extended nodes are compared with the parent layer nodes to quickly avoid closely arranged obstacles, and multiple child nodes are generated at one time through node expansion to improve the node expansion ability. At the same time, node expansion method and delay distance formula can be well integrated with A* algorithm. Finally, the test verifies that the algorithm can spend less time to find the shortest path.

Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides a comprehensive summary and systematic review of vibration signal-based GFD methods in recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults and their vibration signal characteristics. The authors overview and compare the common feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, and entropy. For each method, this paper introduces its idea, analyses its advantages and disadvantages, and reviews its application in GFD. Then the authors present machine learning-based methods for gear fault recognition and emphasise deep learning-based methods. Moreover, the authors compare different fault recognition methods. Finally, the authors discuss the challenges and opportunities towards data-driven GFD.