IET Collaborative Intelligent Manufacturing最新文献

In the last decays, manufacturing systems evolved to meet the high product variety required by the market. Different products can be manufactured in the mixed-model assembly lines, with an increase in the process complexity. In these production systems, the required flexibility is mainly provided by operators in the final assembly stages. Here, human errors could lead to high economic losses. A lack is observed in available research concerning a formal quantification of manufacturing complexity considering the joint effect of shape complexity and similarity in the mix variety. This paper focuses on operator decision-making in 2D object recognition tasks, since this is the most critical task performed in mixed model assembly systems. A novel model to quantify the information content in 2D object recognition task is proposed. The model is based on the Shannon's Entropy theory and considers both shape complexity and object similarities. Numerical experiments are provided, and results obtained show the effectiveness of the model in capturing the joint effect of shape complexity and similarities on the task information content. The proposed model can be adopted in a production environment for re-allocating tasks/sub-tasks to avoid the high amount of information to be processed affecting operators' performance.

Data-driven fault diagnosis has prevailed in machine condition monitoring in the past decades. However, traditional machine- and deep-learning-based fault diagnosis methods assumed that the source and target data share the same distribution and ignored knowledge transfer in dynamic working environments. In recent years, knowledge transfer approaches have been developed and have shown promising results in intelligent fault diagnosis and health management of rotary machines. This paper presents a comprehensive review of knowledge transfer approaches and their applications in fault diagnosis of rotary machines. A problem-oriented taxonomy of knowledge transfer in fault diagnosis is proposed. The knowledge transfer paradigms, approaches, and applications are categorised and analysed. Future research challenges and directions are explored from data, modelling, and application perspectives.

Organisations all over the world are going through the process of digital transformation (DT). Enterprise Architecture (EA) is a method and an organising principle that aligns the business's objectives and strategies with the Information Technology strategy and execution plan. EA provides a guide to direct the evolution and transformation of enterprises with technology. The EA principles are one of the key concepts in the definition of EA; they assist in recognizing the organization vision and validating the outcomes. However, the lack of adequate instruments for assessing the current state and identifying opportunities for EA management procedures improvement often leave organisations unsure of where to begin improving their procedures. The aim of this paper is to help organisations identify these improvement opportunities. To do so, a decision model was developed to evaluate the influence DT technologies have on the EA principles proposed by The Open Group Architecture Framework (TOGAF). A literature review was conducted, and five main DT Technologies applied in the EA scope were identified. With that, a decisional model was created based on two decision-making methods called Decision-Making Trial and Evaluation Laboratory and PROMETHEE. The 21 architecture principles proposed by TOGAF were evaluated and the influence the technologies exercised on the principles were identified. As a result, Big Data and Cloud Computing technologies were indicated as having the greatest effect over the analysed principles, therefore concluding that when applied in the EA scope, these technologies can help organisations improve their EA procedures.

This study proposes a methodology that integrates Weighted Goal Programing with the Fuzzy Analytical Hierarchy Process to obtain the product mix in a multi-bottleneck system. The problem is approached by analysing a case of a company that manufactures four products that must pass through six workstations. The opinion of four specialists involved in the decision is considered and goals are set contemplating profit maximisation, the balance between workstations, exploitation of bottleneck resources and customer satisfaction. The prioritisation of these objectives is obtained through the Fuzzy Analytical Hierarchy Process. This methodology takes into account the uncertainty in the evaluation of the experts. From its application, a single crisp vector is obtained, which is transformed into the weights of the goals. The result is a product mix that satisfies the goals, corresponding to the experts' opinions.

In the ship design, there are strict vibration-proof requirements for precision instruments. Therefore, a ship repulsive magnetic levitation damping device is designed to achieve vibration reduction. And one self-tuning predictive control method is proposed to achieve the stable levitation of this device. Firstly, a predictive control (MPC) method with state constraints and input constraints is adopted to realise the stable suspension of the floater. The MPC can solve the problem of position imbalance of the magnetic levitation system under the external complex disturbances. Secondly, a self-tuning MPC method based on recursive least square is proposed to solve the problem caused by the fixed parameters of the traditional predictive controller. At the beginning of each control cycle, the recursive least-squares (RLS) method is used to estimate the parameters of the system. Thus, the optimal control model could be obtained for the current situation. Then, this model is applied to the predictive controller to solve the problem of parameter fixation in the traditional predictive control. Finally, the simulation results show that it can improve the accuracy, dynamic response and anti-interference performance obviously.

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.