Mutation and selection in industrial application
Optimization tasks in the area of production control, mixture optimization of chemical substances, positioning of components in deep-drawing presses and optimization of the geometry of self-pierce rivets have shown the potential of the Evolution Strategy in industrial applications. But, also components of looms, coffee blends, glaze blends for tiles and sanitary ceramics and the shape of headlight mirrors as well as formulas for the description of rubber seals have been optimized by inpro employees. In many of these questions, the usual mathematical optimization methods proved to be less suitable. The Evolution Strategy often finds better solutions with greater certainty.
Planning and implementation of optimization
When planning optimization, the effort involved in realizing and evaluating the individual variants must be considered. This applies to optimizations in the computer as well as to those where real models are built. If, for example, the intake manifold of a vehicle is to be optimized, it would be too expensive to build a new intake manifold for each variant. An adjustable intake manifold, on which the most important influencing variables can be changed, is the answer. In the computer model, it is not a solution to change every node of a finite-element network. Since a component can have several millions of such nodes, optimization is not possible without further effort. Here it helps to change the design of surfaces that are described with a few parameters and thus to obtain an object that can then be optimized.
The evaluation of a variant can take a lot of time. For example, the most accurate method for calculating the properties of a weld seam requires several hours of computing time for a seam length of a few centimetres. This is of course much too long if several meters of weld are to be optimized. But it does not always have to be as accurate. An alternative method developed by inpro now only requires a fraction of this computing time and provides enough prediction accuracy – this now also makes it possible to optimize weld seams. In practice, it is possible and sensible to first work with a simplified method and then switch to a more accurate and more computationally intensive method in the final optimization phase.
When optimizing the mixture of chemical substances, it took one laboratory day per offspring to measure the physical properties of the mixture. An additional bionic process was used to remedy this situation. By means of artificial neural networks – simple replicas of brain structures in the computer – it was possible to predict the physical properties of the offspring without further laboratory measurements. The networks were first taught with examples – if this mixture is present, it has these physical properties; if that mixture is present, it has those properties. Then the networks were able to predict with enough accuracy the properties of mixtures that had never before been perceived.