Abstract
Because time to market becomes shorter the proof of design quality of brake systems needs to be moved into the virtual design process. CAE-based robustness evaluation, CAE-based robust design and CAE-based quality control including minimization of brake noise become more and more an important part of virtual prototyping. Because different frequencies of excitations needs to be taken into account the design and proof of deterministic design load cases, which ensure enough safety distances are difficult or not possible to derive. The alternative to deterministic design load cases is the direct investigation of robustness, robust design and quality control of the brake design in the windows of expected input variation, which are a result of scatter of environmental conditions and production tolerances. Efficient virtual methodology to investigate the robustness is using stochastic analysis to define the uncertainties, create and run the samples and measure the design robustness in terms of probabilities of violating noise excitation levels. Because every design evaluation in the virtual world still needs significant amount of time it is a challenge to balance between the definitions and discretization of uncertainties, the reliability of stochastic analysis methodology and the reliability of the results of variation and correlation using a minimum of design evaluations. After reliable measurements of robustness can be calculated these measurements are the bases to quantify the robustness of brake design as early as possible in the product development process. Using robustness evaluations selected hardware and test conditions at important gates of the product development are validated. Furthermore the identified sensitivities to important sources help to design worst case test configurations for virtual evaluation as well as hardware test cases. Therefore robustness evaluation can be a very valuable part of virtual prototyping to achieve robust designs fulfilling the quality requirements under all expected environmental uncertainties of brake production and brake use.
KEYWORDS - CAD-based robustness evaluation using stochastic analysis, robust design, CAE-based quality control