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Due to technological advances in science, the complexity of processes under investigation increased continuously in recent years. For example, in mechanical engineering, there are often highly nonlinear input-output relationships combined with a large number of constraints. Sheet metal spinning is one example of such a production process. Many flexible and powerful methods to global optimization have been developed. In this paper, a sequential approach originally developed for computer experiments will be adopted and applied to optimize the spinning process based on physical experiments. This approach sequentially refines the model by adding new design points based on the expected improvement criterion. This criterion balances the need to observe at the predicted optimum with the need to investigate the design space in areas of high uncertainty. However to guarantee an efficient optimization of the spinning process, this approach has to be embedded in a more substantial procedure. One reason for this is the liability of workpiece failure for most of the operable design space. Since the shape of the failure region is unknown, many missing observations have to be expected when exploring the design space. The other reason is the need to incorporate available process knowledge of sheet metal spinning to improve the efficiency in optimization. The main problem in implementing this information is a changing process behavior for different geometries and materials used. Hence, if a component with a new geometry has to be optimized, it is difficult to include available process knowledge. In this paper, an adaptive sequential optimization procedure (ASOP) is presented to cope with these problems in order to guarantee an efficient optimization of such complex processes. The approach is exemplified by optimizing the spinning process for a fixed geometry.

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SUMMARYSheet metal spinning is a very complex forming process with a large number of quality characteristics. Within the scope of a joint project of the Department of Statistics and the Chair of Forming Technology the impact of process parameters (design factors) on important quality characteristics has been investigated both theoretically and experimentally. In the past, every response has been treated individually and uncontrollable disturbances (noise factors) have been neglected. Now this approach has been extended to robust multiresponse parameter design. For this, a review of common multivariate approaches for robust parameter design has been carried out, which also leads to the proposal of some new variants. In addition to the theoretical comparison, the methods were applied to data gained in the sheet metal spinning process. The obtained results were evaluated in terms of applicability, limitations and quality accuracy. Practical experiments confirmed the high degree of efficiency that the finally proposed method based on desirabilities promises.

The aim of this study was to investigate clinically-relevant properties of hybrid-, nano-, and nanohybridcomposite and to compare them to a silorane based composite. The performance of all tested materials strongly depended on the mechanical properties, emphasizing the importance to adjust the measuring conditions to the oral environment. All selected classes of restorative composites showed non-uniform properties. Not all nanohybrid composites proved generally superior to the other classes of composites. The methacrylat free Filtek™ Silorane showed low polymerization shrinkage and average mechanical properties.