首页期刊导航|Concurrent engineering: research and applications
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Concurrent engineering: research and applications
Academic Press
Concurrent engineering: research and applications

Academic Press

季刊

1063-293X

Concurrent engineering: research and applications/Journal Concurrent engineering: research and applications
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    Decision-making solutions based artificial intelligence and hybrid software for optimal sizing and energy management in a smart grid system

    Ferdaws Ben NaceurSana ToumiChokri Ben SalahMohamed Ali Mahjoub...
    3-19页
    查看更多>>摘要：This paper describes a decentralised smart grid system containing renewable energies, storage systems and distributed generation with human control and intervention. The importance of each element and the interaction between them leads to think about a decision-making strategy. In fact, the integration of a Photovoltaic Panel (PVP) is used due to its availability and its participation in the carbon emissions reduction. Also, a battery is required to fill a power gap or absorb extra generated energy. Moreover, an optimal sizing is needed to get an efficient system with minimum cost. Also, an energy management strategy (EMS) is essential to ensure the power resources scheduling in order to keep a continuous equilibrium supply-demand of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. In the first part, the proposed smart grid optimal sizing is determined under real weather data (solar radiation) of the city of Sousse, Tunisia, using the Hybrid Optimization of Multiple Energy Resources (HOMER) software technique. This approach is chosen thanks to its simplicity, effectiveness, and high precision compared to traditional techniques. In this paper, several configurations (Grid, (Grid-battery), (Grid-PVP), (Grid-PVP-battery)) are studied. The obtained results prove that the (Grid-PVP-battery) system configuration is the most efficient and economical solution. In the second part, a robust energy management strategy (EMS) is proposed for two smart grid configurations (grid-battery, grid-PVP-battery). This strategy is based on Fuzzy Logic Control (FLC) thanks to its non-linear modelling and its ability to make decisions relating to energy management. The primary goal of the suggested (EMS) is to ensure the energy resources scheduling in order to keep a continuous equilibrium among the production and consumption of electricity and avoid instabilities in the grid, with guaranteeing a minimum cost of electricity. As input data, (FLC) used time-varying price electricity (Price (t)) to solve an instant decision problem by choosing, at each instant, the optimal energy source (which provide electricity at the cheapest price possible). The obtained results, carrying out Matlab simulation, prove the efficacy of the proposed strategy, not only, in the energy resources scheduling to meet the load, but also, for the system cost reduction since the PVP has been used as much as possible since it is inexpensive relative to the costs of battery capacity and the grid.
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    Sensitivity study of process parameters of wire arc additive manufacturing using probabilistic deep learning and uncertainty quantification

    Thinh Quy-Duc PhamVan-Xuan Tran
    20-33页
    查看更多>>摘要：This study employs a deep learning (DL) based stochastic approach to comprehensively interpret the effects of current intensity and velocity variations on temperature evolutions and cooling rates in the wire arc additive manufacturing (WAAM) process of a thin wall. Uncertainty raised from process parameters, material properties, and environmental conditions significantly impacts the final product quality. Furthermore, understanding the relationship between the process and temperature evolution within the WAAM process is complex. This study contributes to quantifying the uncertainty to the final product quality, such as temperature evolutions and cooling rates via a fast and accurate DL-based surrogate model. This contribution helps to precise adjustments and optimizations to enhance the overall WAAM process. Initially, a DL-based surrogate model is constructed using data obtained from a high-fidelity validated finite element (FE) model, ensuring an impressive 99% accuracy compared to the FE model while reducing computational costs. Subsequently, probabilistic methods are used to characterize uncertainties in current intensity and velocity, and the Monte-Carlo method is applied for uncertainty propagation. The findings illustrate that small variations in the input parameters can lead to significant fluctuations in temperature evolutions. Additionally, a sensitivity analysis is conducted to precisely quantify the influence of each input parameter. Finally, an uncertainty reduction is performed to enhance the variation of cooling rate. In general, this study is expected to make precise adjustments and optimizations to enhance the overall WAAM process for better quality of printed piece.
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    Industry 4.0 implementation barriers in automotive manufacturing industry: Interpretive structural modelling approach

    Radhe Shyam OjhaAmit KumarVineet KumarAvinash Ravi Raja...
    34-45页
    查看更多>>摘要：Industry 4.0 manufacturing practices are the trends of automation and data exchange, which involve heavy investment to adopt the latest modern technologies for improving the manufacturing process in terms of productivity, efficiency, flexibility, and profitability. In this context, the current study focuses on the key implementation barriers of Industry 4.0 in the automotive industry. An interpretive structural model of the barriers to implement Industry 4.0 in the automotive industry has been proposed to create a hierarchical order of key barriers, find the relationships among them, and create a graphical model of the system. Based on the review of available literature and discussion with experts in the automotive field, thirteen barriers have been identified to frame the model. Further, out of these thirteen, two barriers have been found to be dependent, another two as driving barriers, the remaining nine as linkage barriers, and none as autonomous barriers. Based on the developed model, 'Great Risk of Obsolescence' has been found to be the top-level dependent barrier and geographical risk to be the lowest-level independent barrier. A clear comprehension of the interactions between the key barriers can help in deciding priorities and managing the same to have higher efficacy and efficiency in implementing Industry 4.0. Overall, we aim to identify Industry 4.0 barriers in the automotive industry and prioritize them during the implementation of Industry 4.0. The structured model, so designed and developed, is expected to facilitate the understanding of the interdependence of the barriers of the Industry 4.0 manufacturing system amid its implementation.
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    Integration of lean manufacturing system with novel intuitive fuzzy syncretic lean frame work to improve the overall equipment effectiveness

    Rita PimpalkarMahadev Madgule
    46-57页
    查看更多>>摘要：Lean manufacturing embraces the principle of doing more with less by removing non-value-added activities from manufacturing processes to preserve effectiveness, flexibility, and profitability. Predictive maintenance in production stages are more critical since it involves machines which are prone to failure and a post failure maintenance stagnates the production which creates bottlenecks due to machine failures. Hence in this work a novel intuitive fuzzy syncretic lean frame work has been implemented to incorporate the need of intelligent systems. The framework is divided into phases which employs Fuzzy logic with embedded smart sensors for effective utilization of man machine and materials in order to improve the effectiveness of a production floor. A novel time based forecasting is done in the design phase which implements the lean tool Takttime. The manufacturing phase uses the sensors to determine the predictive maintenance of the machines thereby implementing continuous flow as the lean tool and the inspection phase uses smart sensor system for real time continuous monitoring of the machining process thereby incorporating Poka-yoke as a lean tool. Thus by implementing the framework the overall equipment effectiveness is achieved which helps in achieving continuous flow of products and defect free products in any production firm.
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    A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

    Vimlesh Kumar OjhaSanjeev GoyalMahesh ChandAjay Kumar...
    58-77页
    查看更多>>摘要：Integration of sophisticated technologies such as Internet of Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing systems (AMS). However, implementation of data-driven decision making in AMS still remains challenging due to data heterogeneity, real-time processing demands, and integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption of DDDM in AMS to enhance its decision-making capabilities. This framework consists of six stages: manufacturing stage, sensing stage, data stage, knowledge stage, decision stage, and application stage. The proposed framework leverages big data analytics to extract actionable insights from diverse datasets, integrates CPS to create a seamless interaction between physical and digital systems, and employs IoT technologies for real-time data acquisition and monitoring. The framework is validated through a comprehensive case study involving a CNC milling machine dataset, demonstrating significant improvements in operational efficiency, decision accuracy, and response time. The case study involves detailed data collection steps, preprocessing, and analysis, showcasing the framework's practical implementation and effectiveness. The results show that the proposed framework addresses existing challenges and provides a scalable solution for DDDM implementation in AMS.
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    Digital twinning of vertical centrifugal casting

    Dhaval AnadkatAnjali DaveAmit SataMinal Shukla...
    78-89页
    查看更多>>摘要：This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation.
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