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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).
Replantation Surgery
Published in C.W. de Silva, Mechatronic Systems, 2007
There is a rational need to develop intelligent mechatronic systems. A degree of intelligence may be incorporated into a mechatronic system in several ways; for example, through intelligent sensing, intelligent actuation and, above all, intelligent control. Often the intelligent behavior is realized by representing the decision-making process of a human expert. To this end, fuzzy logic has been proved to be an effective approach. System modeling has many uses, including computer simulation, design, evaluation, and control. In particular, models are indispensable in model-based control. Mechatronic systems are by and large nonlinear, and their modeling is rather challenging, and so is the problem of model-based control.
Introduction and Background
Published in Haym Benaroya, Mark Nagurka, Seon Han, Mechanical Vibration, 2017
Haym Benaroya, Mark Nagurka, Seon Han
System modeling is the process of approximating physical characteristics in terms of mathematical expressions, generally consisting of one or more equations. Mathematical analyses can then be brought to bear to “solve” the equations.19 Solving the equations eventually results in a prediction of model behavior. The engineer’s task is then to study these predictions and make sure they make sense physically. It is also to make sure that a design based on the predictions can be created and is safe.
Assessing the economic and technical efficacy of grid-connected photovoltaic systems through a combination of mathematical and deep learning models with experimental validation: A case study
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2023
Reference (Kazem et al. 2022) provided an extensive analysis of the current state of research on design modeling, algorithms, and software used in PV systems. The authors employ a systematic review methodology to examine a wide range of literature on PV system design, modeling techniques, optimization algorithms, and software tools. The paper encompasses a comprehensive analysis of relevant research articles published up to the time of the study. The review highlights various aspects related to PV system design, including system components, such as PV modules, inverters, batteries, and charge controllers. It also addresses system modeling approaches, such as mathematical models, empirical models, and simulation techniques. Furthermore, the review explores optimization algorithms used for PV system design and highlights their applications in maximizing energy production and system performance. The authors discuss the importance of software tools in PV system design and analysis. They review various software platforms and tools commonly used in the field, examining their features, capabilities, and limitations. The study presents a critical evaluation of these tools, enabling researchers and practitioners to make informed decisions regarding software selection for their PV system projects.
Analysing risk to function fulfilment: applying the function integrity diagnosis and documentation method
Published in Journal of Engineering Design, 2021
Robert Lawrence Wichmann, Kilian Gericke, Boris Eisenbart
There are software implemented system modelling tools such as Multiple Design Structure Matrices (MDSM) linking multiple domains such as function to component (Eichinger et al. 2006) and the Object Management Group’s Systems Modelling Language (SysML) (OMG 2012). These tools offer formal verification of design and even predictive capabilities but they require a long learning curve for effective use (Eng et al. 2017). For example, SysML encompasses representations for system specification, design, analysis, validation and verification which allow it to support a complete design process (Eisenbart et al. 2015). However, there are gaps in modelling flexibility, tracking of interfaces and weaknesses in interdisciplinary communication (Hampson 2015) making SysML models challenging to apply in early conceptual design (Chandrasegaran et al. 2013).