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Manufacturing Systems: Modeling and Simulation
Published in Naim A. Kheir, Systems Modeling and Computer Simulation, 2018
The manufacturing system design can be influenced by the use of modeling and simulation of the factors listed in Sec. 7.2.1. Design issues relating to line balancing, capacity, mix, quantity of machinery, the effects of automation, material handling, staffing levels, and logical operating policies are all areas in which simulation can have a dramatic impact on the system design. Given that modeling can be performed before actually building new systems or interrupting existing systems, modeling and simulation can provide a very cost effective way to develop system design requirements. Employing detailed simulation models early in the planning phase of a project can yield equipment and system performance criteria used in the design requirements for engineers, planners, and vendors. Simulation models have also been used to assist in preparing the factory for upcoming changes, guiding installation and start-up, and debugging the actual system as it comes on line (Kalasky and Schafer, 1987).
Introduction
Published in Nayef Ghasem, Modeling and Simulation of Chemical Process Systems, 2018
Modeling and simulation is important in research. They are strictly related computer applications, which is a key factor in science and engineering. They help to reduce the cost and time needed for research. Modeling and simulation are useful tools for engineers to understand processes easily. The topic helps to understand the behavior of a dynamic system and how the various components of that system interact. Modeling and simulation are employed to test the reactions of the system in a cost-effective manner instead of carrying out experiments and observations on the real system. Modeling is the process of building mathematical models using different equations that can be analyzed and solved using computer software; simulation is the evaluation of the systems performance using a computer model. Modeling and simulation are vital in research; they represent the real systems either via physical reproductions on a smaller scale or via mathematical models that allow representing the dynamics of the system. Simulation permits exploring system behavior in a way that, in the real world, is frequently either not possible or too risky. A simulation of a system is the operation of a model, which is a representation of that system.
Application of Unique Processes
Published in Lory Mitchell Wingate, Systems Engineering for Projects, 2018
Modeling and simulation are methods of using both representative model and activity simulations to demonstrate the behavior of a system or elements of that system. Modeling and simulation techniques provide a way to demonstrate how the system may look or behave under different conditions. This is a significant risk reduction strategy, because how a system engages and interacts within two or more elements, all the way through the full system interactions with its operational environment, are often unknown until each element is integrated and/or the whole system is deployed. Having a model that can be used to run simulations will provide a platform upon which insights into patterns of straightforward and emergent behavior are brought to light and where the system and all its elements can be optimized.
Examples of performance requirement derivation through modelling and simulation in complex projects
Published in Australian Journal of Multi-Disciplinary Engineering, 2023
Garth De Visser, Stephanie Knight
Amongst many other applications, modelling and simulation serves as a key enabler in the early-stage concept design and systems engineering of large and complex projects. Large materiel acquisition projects, particularly in Defence, necessitate upfront work in the development of concepts for the basis of the investment, the capability required, the associated operational impacts, and the translation of these elements to system functionalities and performances. Such concept development work, undertaken from a solution-agnostic point of view, provides an independent basis for the evaluation of proposed or identified materiel options for meeting the capability needs. Many studies over the past several decades have continued to reinforce the importance of investment in the upfront concept design stages of large complex projects, particularly through the employment of systems engineering practices, in order to not only assure the design, but also to save time and cost over the life of the program (INCOSE 2015).
Robustness and performance evaluations for simulation-based control and component parameter optimization for a series hydraulic hybrid vehicle
Published in Engineering Optimization, 2020
Katharina Baer, Liselott Ericson, Petter Krus
The design of complex products is increasingly based on, or supported by, modelling and simulation, for example already in the early design stages to explore design alternatives. This is often aided by numerical optimization to explore either entire systems or sub-aspects in the design process. In the case of vehicular technology, and hybrid vehicle applications in particular, this typically addresses the optimal control of a transmission. In a hybrid transmission, the control decision at the highest level concerns how a given power demand is satisfied by multiple power sources. But optimization can also serve to explore wider design problems and potentially include component sizing, topology and technology choices, all of which require coordination and harmonization, and increase the complexity of the design problem.
The modelling and operations for the digital twin in the context of manufacturing
Published in Enterprise Information Systems, 2018
Jinsong Bao, Dongsheng Guo, Jie Li, Jie Zhang
In order to enhance the capacity of product digital design and manufacturing, digital modelling and simulation technologies have been developed rapidly in the past 20 years. Traditional static models are constructed before production, including product geometric model (Chu, Wu, and Hsu 2009), feature-based model (Li et al. 2006), product structural model (Wu and Hsu 2008) and product integration model (Coulibaly, Mutel, and Ait-Kadi 2007). However, considering the complex and ever-changing production environment, uncertain disturbances may lead to the failure of the product models. Additionally, the introduction of concurrent engineering (Li, Zhang, and Chen 2001) and collaborative manufacturing (Mu, Bénaben, and Pingaud 2013) enhanced the capability of information sharing in the product design phase. Nevertheless, the product models are relatively independent at individual phases, i.e. product design, manufacturing and service phases. Product data cannot be communicated and shared between design, process, manufacturing and service models, resulting in the problem of information island.