Making design decisions is characterized by a high degree of uncertainty, especially in the early phase of the product development process, when little information is known, while the decisions made have an impact on the entire product life cycle. Therefore, the goal of complexity management is to reduce uncertainty in order to minimize or avoid the need for design changes in a late phase of product development or in the use phase. With our approach we model the uncertainties with probabilistic reasoning in a Bayesian decision network explicitly, as the uncertainties are directly attached to parts of the design artifact^′s model. By modeling the incomplete information expressed by unobserved variables in the Bayesian network in terms of probabilities, as well as the variation of product properties or parameters, a conclusion about the robustness of the product can be made. The application example of a rotary valve from engineering design shows that the decision network can support the engineer in decision-making under uncertainty. Furthermore, a contribution to knowledge formalization in the development project is made.

The design of components suitable for manufacturing requires the application of knowledge about the manufacturing process chain with which the component is to be manufactured. This article presents an assistance system for decision support in the context of design for manufacturing. The assistance system includes explicit manufacturing process chain knowledge and has an inference engine that can automatically evaluate the manufacturability of a component design based on a given manufacturing process chain and resolve emerging manufacturing conflicts by making adjustments on the component or resource side. A link with a CAD system additionally enables the three-dimensional representation of derived manufacturing stages and manufacturing resources. Within the assistance system, a manufacturing process chain is understood as a configurable design object and is implemented via a constraint satisfaction problem. Furthermore, the required abstraction of manufacturing processes within finite domains can be reduced to the extent that necessary modeling resolution is achieved by incorporating empirical or simulative surrogate models into the CSP. The assistance system was conceptually validated on a tailored forming process chain for the production of a multimaterial shaft and provides added value, as valuable manufacturing information for component designs is automatically derived and made available in explicit form during the component development.

For an optimal preparation of mechanical engineering students for their future work life, the use of problem-based methods in design teaching is investigated. Therefore an intelligent tutoring system for computer aided design education will be developed, which can automatically evaluate computer aided design models of design students. A knowledge-based engineering system will be used to assistance the design students in the execution of design tasks. Using a practice-oriented example, the application and the advantages for teaching will be verified and discussed.