IET Collaborative Intelligent Manufacturing最新文献

Cell formation and machine layout in cellular manufacturing systems (CMs) design are considered as a crucial, yet hard and complex decision process. Owing to the nondeterministic polynomial time (NP) and combinatorial class of this problem, this paper presents an innovative heuristic approach to re-arrange machines enabling the minimisation of inter/intra- cellular movements as well as the cost of material handling between machines, therefore increasing group efficiency and efficacy. The heuristic approach, which is based on group technology, genetic algorithms, and desirability function, determines the optimal solution for flexible cell formation and machine layout within each cell. Flexibility refers to an explicit improvement using the desirability function to modify cell design by altering the ratio data; that is, the weight factor to meet demand flexibility. Specifically, the desirable function proposed here to provide the optimal setting of the weighting factor as a key factor which enables CMs design the flexibility to control the cell size. Promised results were obtained when the proposed approach was applied to a case study. Practical implications and recommendations are provided for use by decision makers in the design of CMs.

With the rapid development of the social economy, consumer demand is evolving towards diversification. To satisfy market demand, enterprises tend to improve competitiveness by providing differentiated products. How to price differentiated products becomes a hot topic. Traditionally, customers' preferences are assumed to be independent and identically distributed. With a known distribution, companies can easily make pricing decisions for differentiated products. However, such an assumption may be invalid in practice, especially for rapidly updating products. In this paper, a dynamic pricing policy for differentiated products with incomplete information is developed. An adaptive multi-armed bandit algorithm based on reinforcement learning is proposed to balance exploration and exploitation. Numerical examples show that the frequency of price adjustment affects the total profit significantly. Specifically, the more chances to adjust the price, the higher the total profit. Furthermore, experiments show that the dynamic pricing policy proposed in this paper outperforms other algorithms, such as Softmax and UCB1.

Industry 4.0 driven by the internet of things (IoT) is changing the way of producing and has been offering smart manufacturing systems with support technologies for the digital transformation of manufacturing plants seeking improvements in productivity, in control over the process, and customisation of production, among others. Due to these technological developments, small and medium-sized industries have been identified as a weak link in adapting their processes and resources, where they are usually the biggest victims in the transition to industry 4.0. The evidence points out that the excess data inserted in the databases of the manufacturing system of the industries influences the decision-making process of managers, making the process more complex and dynamic. This research focuses on a systematic literature review to assess how data-based performance measurements for machines are being handled in the context of industry 4.0. The methodological approach follows the application of the PROKNOW-C (Knowledge Development Process-Constructivist) method used to build a Bibliographic Portfolio in a structured way in line with the research theme. The results presented in the Bibliometric Analysis enabled the construction of a performance measurement model based on the sources of the researched articles.

In this work, we present a cloud-based digital twin for monitoring of a clamping technology for machining of composite parts. Supporting large and/or freeform composite parts is crucial to avoid bending during drilling. Bending of the part will lead to delamination and frayed edges of the drilled holes. The new active clamping technology allows to realise a stabilised fixture, localised in the area where the drilling occurs, to avoid bending. This significantly improves the quality of the drilled holes. The clamping device is equipped with an IoT edge device, with a bidirectional communication to the cloud. The cloud-based digital twin analyses the quality of the drilled holes based on computer vision, monitors the drill wear and detects incorrect operation of the active clamping device. All data is stored in the cloud. By means of a knowledge graph, which acquires and integrates information into an ontology and provides a central information access, it will be easier for a data scientist to query this data and to gain new insights in the operation of the drill with active clamping device. The full deployment occurs on the Microsoft Azure cloud platform. This transforms the standard machine into an Industry 4.0 compliant machine.

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.