Knowledge-Based Modeling of Manufacturing Aspects in Structural Optimization Problems

Abstract

Abstract. In this paper we present a method for the multidisciplinary optimization of structures including qualitative expert knowledge. In addition to multi objective and discrete tasks, which are solved with a genetic algorithm, mainly expert knowledge and experience is available for certain influences in early design stages. Fuzzy Rule Based Systems (FRBS) provide a powerful tool to model such influences via qualitative human knowledge. Based on this idea, a method for building qualitative, knowledge based models has been developed at the institute and enhanced. As an example, structural components constructed from composite aluminum profiles with embedded continuous reinforcing elements have been optimized.

Divergent Exploration in Design with a Dynamic Multiobjective Optimization Formulation

“…As the name suggests, the exploration value to effort ratio is defined as ϵ ≡ value effort (16) where value can be defined as any individual metric or combination of the metrics defined above. For instance, value could be assessed by the best design alternative with Eq.…”

“…7-10 Accordingly, several researchers have begun to address the difficulties of implementing numerical optimization techniques during this stage of product development. For example, to capture and represent qualitative objectives, researchers have turned to interactive genetic algorithms, [11][12][13][14] fuzzy logic systems, 15,16 and preference based modeling. 17 Concept selection via multiobjective optimization is possible by generating and analyzing an s-Pareto frontier, which is the collection of non-dominated designs from a set of concepts.…”

Fuzzy Modeling of Imprecise Material Parameters in the Analysis of Metal Matrix Composite Structures

“…Imprecise models from expert knowledge Knowledge and experience of experts is often not quantifiable in traditional numerical models, yet important for design and analysis. This qualitative knowledge can be modeled using fuzzy systems [12,18] striving towards a complete description of the engineering problem at hand. Expert knowledge is assessed in language, which itself is vague and imprecise and also known as linguistic uncertainty.…”