Abstract: Fine machining processes are of great importance in automotive series production, e.g. the machining of valve guide and seat in the cylinder head of a combustion engine. In industrial manufacturing processes, disturbances are inevitable and provide a measure of uncertainty in each production step. Increasingly, the influence of such uncertainties is being evaluated using simulation models. In this paper, a modeling approach for simulation of multi-stage fine machining processes with step tools is presented and investigations regarding influence of uncertainty caused by disturbances are performed.
Abstract: The reaming process normally takes place at the end of manufacturing processes when a lot of value has already been added. Therefore, reaming plays an important role for the quality of the finished product. To achieve this high quality, the occurring process errors caused by the machine tool and the reamer or incorrect workpiece handling have to be minimised. Measured data of the reaming process allow the prediction of occurring process errors without the need to evaluate the bore with a coordinate measuring machine. However, manufacturing is already completed at this stage and the correction of errors is either no longer possible or very costly. This paper presents an approach to detect axis offsets within the entry phase of the reamer by analysing the process forces. The calculated offset is then compensated by adjusting the nominal value of the motion control.
Abstract: The task of tolerance analysis usually addresses the question of the mechanical mountability of an assembly. We extend this viewpoint when talking about directional control valves in a crossdomain tolerance analysis; an analysis whose task is to determine the possible variation in the key product characteristics induced by a specific tolerance concept. As the available information about the noise factors to be toleranced is almost always imperfect generalised methods for their representation and the propagation of their impact on the key product characteristics are required. In this study the capabilities and potentials of belief and plausibility measures as well as fuzzy random variables are compared to traditional worst-case and statistical tolerance analysis.
Abstract: Quantification of uncertainty in technical systems is often based on surrogate models of corresponding simulation models. Usually, the underlying simulation model does not describe the reality perfectly, and consequently the surrogate model will be imperfect.In this article we propose an improved surrogate model of the vibration attenuation of a beam with shunted piezoelectric transducers. Therefore, experimentally observed and simulated variations in the vibration attenuation are combined in the model estimation process, by using multi--layer feedforward neural networks. Based on this improved surrogate model, we construct a density estimate of the maximal amplitude in the vibration attenuation.The density estimate is used to analyze the uncertainty in the vibration attenuation, resulting from manufacturing variations.
Abstract: This paper presents the application of a new method for the identification and quantification of interval valued spatial uncertainty under scarce data.Specifically, full-field strain measurements, obtained via Digital Image Correlation, are applied in conjunction with a quasi-static finite element model.To apply these high-dimensional but scarce data, extensions to the novel method are introduced.A case study, investigating spatial uncertainty in Young's modulus of PA-12 parts, produced via Laser Sintering, shows that an accurate quantification of the constituting uncertainty is possible, albeit being somewhat conservative with respect to deterministic values reported in literature.