Ontology-assisted system modeling combines classic system-theoretical modeling with an ontological system specification. Different dynamic system behavior is modeled in configurable basic models with defined input and output interfaces. Basic models are organized in a model base (MB). The ontology is used to specify a set of modular, hierarchical system structures using references to basic models in the MB. Moreover, the ontological model defines possible parameter settings of referenced basic models. Thus, the ontology describes a set of different system configurations for a specific domain. A base ontology for mapping such problems is the System Entity Structure (SES). A combination of SES ontology with a MB for system modeling and goal-oriented model generation was introduced with the SES/MB framework. Starting with the basics of SES ontology and SES/MB framework as well as the discussion of some extensions, a new SES toolbox for ontological modeling within the MATLAB/Simulink environment is presented. The toolbox architecture is then discussed. The main focus in this regard is on the graphical SES editor, the toolbox methods and the seamless integration with MATLAB/Simulink. The latter is described by means of deriving a specific system model from the formal specification and the automatic generation of a corresponding executable MATLAB/ Simulink model.

In engineering, a broad range of environments exist for modeling and simulation with integrated parameter optimization. The established techniques only optimize model parameter values, the model structure is considered to be fixed. As system performance is optimized, one may have to redesign the model structure. The redesign is done manually by an analyst. The suboptimal combination of automatic parameter optimization and manual structural changes leads to an optimization task that is prone to error. This paper details an approach that provides optimization through automatic reconfiguration of both the model structure and model parameters. An optimization method that uses an evolutionary algorithm is supported by a model management method. This method is based on the system entity structure/model base framework. The admissible model structures and their associated model parameter sets are specified using the system entity structure ontology. Basic dynamic model components are organized in a model base. In addition to this, new algorithms are introduced. These map knowledge coded in the system entity structure to a set of numerical (structure) parameters, and also perform this mapping in reverse. In this manner a combined structure and parameter optimization problem is derived. Since both methods – evolutionary algorithm and model management – work together concurrently, different system configurations can be evaluated automatically. The objective is to provide an optimal solution; a model optimized for both parameter and structure.

Whereas in the past the sustainable use of resources and the reduction of waste have mainly been looked at from an ecological point of view, resource efficiency recently becomes more and more an issue of cost saving as well. In manufacturing engineering especially the reduction of power consumption of machine tools and production facilities is in the focus of industry, politics and research. Before power consumption in machining processes can be reduced it is necessary to quantify the amount of energy needed, to identify energy consumers and to determine the available degrees of freedom for an optimization. Simulation can be an adequate alternative to the measurement of power consumption during machining operation. However, many of the available simulation methods are not suitable for this task. This paper describes an approach based on the discrete-event simulation, which is known mainly from the simulation of logistical systems. It has been adapted to model machining operations and to generate workpiece-specific power consumption profiles and energy footprints. Two-axis turning in a CNC machining centre is shown exemplary. The aim is to provide a basis for further applications such as the simulation, comparison and optimization of power consumption in process chains and production systems in combination with logistical models.

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The increasing complexity of systems entails an increasing complexity of simulation models. Likewise, heterogeneity in system components corresponds to heterogeneous simulation models. Cyber physical systems (CPS) represent an emerging class of technical systems characterized by their complexity and heterogeneity. Developing simulation models for CPS brings various challenges, one of which is determining the simulation fidelity. Fidelity evaluation can be introduced as the degree to which a simulation model matches the characteristics of the system it represents. Due to the growth of system complexity in CPS, the number of test cases required to reach admissible coverage to assure adequate simulation fidelity is very high. Along with that, heterogeneity in system components comes on top as another challenge. Therefore, adaptability, flexibility and automation can be identified as the key characteristics of a fidelity evaluation approach that determines its success. Model-based testing (MBT) advocates the use of models for the specification of test cases and proposes workflows for automatic test case generation. This paper presents an MBT approach for objective fidelity evaluation of complex, modular simulation models. The methodology implies that appropriate data for fidelity evaluation are available. Each test model is represented according to the formal structure of experimental frame (EF). For generating an executable EF for a model under test (MUT), configurable basic models are provided by a model base (MB). In the same manner, configurable basic models for composing various MUTs are stored in the MB. The system entity structure (SES) ontology is used for the specification of a family of MUT and test model designs on an abstract level. This means that the SES describes a set of various MUT and test model structures and parameter settings. Using the SES and MB, a specific executable model consisting of an MUT and a test model can be generated. Based on these ideas an infrastructure implementation for automated fidelity evaluation of complex, modular simulation models within MATLAB/Simulink is proposed in this paper.