Abstract: This paper proposes a Behaviour Prediction Framework with an objective to help designers tackling the problem of uncertainty emerging from system architecture and the effects of the uncertain operating conditions. The proposed framework combines structural and dynamic system model. The Design Structure Matrix is applied to model structural arrangements and dependencies between the subsystems. The Model Predictive Control is applied to model the system in discrete and continuous dynamic domains. As the result of the proposed framework, stability analysis of subsystems in interaction become possible and feedback on system architecture could be provided. To test validity of the proposed approach, the test case involving climate chamber with heat regeneration is presented.
Abstract: We present a second order approximation for the robust counterpart of general uncertain nonlinear programs with state equation given by a partial di erential equation.We show how the approximated worst-case functions, which are the essential part of the approximated robust counterpart, can be formulated as trust-region problems that can be solved effciently using adjoint techniques. Further, we describe how the gradients of the worst-case functions can be computed analytically combining a sensitivity and an adjoint approach. This methodis applied to shape optimization in structural mechanics in order to obtain optimal solutions that are robust with respect to uncertainty in acting forces. Numerical results are presented.
Abstract: Development of a complicated technical problem encompasses different successive decisions that are based on techical analysis and engineering judgment. In each of these steps, somedegree of non-determinism is inevitable. The paper discusses different types of non-deterministic parameters that may be relevant in different stages of engineering analysis. The entire cycle of development of a product is considered, and it is shown that the relevance of methods isdifferent in different stages of the development cycle. A classiﬁcation of different types of non-deterministic properties is presented. Based on the nature of these different classes of model properties, it is discussed to what degree each of these ﬁts in the framework of either a probabilistic or a non-probabilistic concept.The availability of realistic data in an appropriate format is another issue that should be taken into account. A validated probabilistic representation is usually only possible after an extensive campaign of data acquisition has been conducted, or at least after suffcient data have been collected to allow for a reliable estimation of a statistical model. A study of scientiﬁc literature shows that validated information is not always available. A general conclusion is that probabilistic methods are applicable in later stages of development, when a suffciently large database of product data has been gathered. Probabilistic approaches are perfectly suited for conditions when the product is already in service. Possibilistic analysis on the other hand is best suited for application in cases when the data set about the product at hand is still incomplete.
Abstract: Today, a wide variety of methods to deal with uncertainty in load-carrying system exists. Thereby, uncertainty may result from not or only partially determined process properties. The present article proposes a classification of methods to control uncertainty in load-carrying systems from different disciplines within mechanical engineering. Therefore, several methods were collected, analysed and systematically classified concerning their characteristic into the proposed classification. First, the classification differs between degrees of uncertainty according to the model of uncertainty developed in the Collaborative Research Centre CRC 805. Second, the classification differs between the aim of the respective method to descriptive methods, evaluative methods or methods to design a system considering uncertainty. The classification should allow choosing appropriate methods during product and process development and thus to control uncertainty in a systematic and holistic approach.
Abstract: The paper reviews the development of reliability assessment in structural analysis under consideration of the non-traditional uncertainty model fuzzy randomness. Starting froma discussion of sources of variability and imprecision, uncertainty models are introduced. On this basis, numerical approaches are displayed for uncertain structural analysis and reliability assessment. Thereby, variations in time are considered which results in a time-dependent reliability measure. Capacity and applicability of the approaches are demonstrated by means of an example.
Abstract: Knowledge about future process properties is crucial for the development of safe and economic products with load carrying structures. Real processes are influenced by uncertainty what causes scattering and deviation from assumed values. As a consequence, products are often oversized or even product failures can occur. To control uncertainty, extensive knowledge about future processes is necessary in the development process. This paper shows an approach for the representation of uncertainty in production-and usage-processes, according to scattering properties and their cause and effect relations. This approach is used as a common platform for storing, locating, comparing and reuse of knowledge about uncertain properties and their relations. The core of the proposed approach is an ontology-based information model with the ability to represent different levels of trusted information in relation to process parameters and cause and effect relations.
Abstract: In the early stages of the vehicle development basic decision for the future vehicle concepts are made. Anyhow not all information for a confirmed decision, especially in the field of tolerance management, is available at that time. Known methods to estimate the impact on the final product normally require determined concepts and nearly completed parts and are thus not feasible to find new solutions. One solution to overcome this problem is the consequent use of design methodology. Combined with tolerance aspects the result of the process is a robust concept for all following process steps.
Abstract: In contrast to Robust Design applications for FEM-modeled parts, the simulation of a mechatronic system including both mechanical and electrical parts requires a different strategy for the investigation of its robustness. Differences mainly results from interactions between the mechanical and the electrical part of the mechatronic system. Furthermore, a comfort mechatronic system in the automotive industry is designed customer-oriented. Subsequently, the behavior of its movement and the respective robustness has to be considered too. This paper presents an approach to evaluate robustness of mechatronic comfort systems. Additionally, the approach is applied to sliding doors of vans to prove the practicability to industrial problems.
Abstract: It is widely accepted that fluctuations in market demands and product life cycles are often unpredictable. Based on these uncertainties, companies cannot calculate with constant demands. Manufacturers are also confronted with quality fluctuations in semi-finished parts that lead to various product qualities. This paper identifies the most relevant uncertainties for companies and gives answers how manufacturers can deal with these problems. It also shows recent developments in the field of flexible forming using servo press technology. Hereby the focus is set on 3D Servo Presses, providing various options for accomplishing uncertainties.