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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