Friedrich Wolf-Monheim

This paper proposes a method for Acoustic Constrained Segmentation (ACS) in audio recordings of vehicles driven through a production test track, delimiting the boundaries of surface types in the track. ACS is a variant of classical acoustic segmentation where the sequence of labels is known, contiguous and invariable, which is especially useful in this work as the test track has a standard configuration of surface types. The proposed ConvDTW-ACS method utilizes a Convolutional Neural Network for classifying overlapping image chunks extracted from the full audio spectrogram. Then, our custom Dynamic Time Warping algorithm aligns the sequence of predicted probabilities to the sequence of surface types in the track, from which timestamps of the surface type boundaries can be extracted. The method was evaluated on a real-world dataset collected from the Ford Manufacturing Plant in Valencia (Spain), achieving a mean error of 166 milliseconds when delimiting, within the audio, the boundaries of the surfaces in the track. The results demonstrate the effectiveness of the proposed method in accurately segmenting different surface types, which could enable the development of more specialized AI systems to improve the quality inspection process.

With the rise of artificial intelligence, the automotive industry searched for novel ways to improve future product design. We focus on designing automatic MacPherson suspension architecture for the automotive sector. It takes time for an automotive engineer to design vehicle parts and thus slows the pace of innovation in this field. Given the car's particular kinematic characteristics, we propose to predict an architecture by positioning the hardpoints. This work deals with the biased data generated using the discipline models using the dataset shift learning paradigm. The optimized data are created with random and uniform sampling, with more samples with random sampling. We resolve the bias in the data, using a novel criterion for tuning the kernel mean matching and a weight estimation algorithm and designing the required target characteristics.

Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by computer simulation using what is called a discipline model. Such a model can be implemented by finite element analysis, multibody systems approach, etc. Designs passing this simulation are then considered for physical prototyping. The overall process may take months and is a significant cost in practice. We have developed a Bayesian optimization (BO) system for partially automating this process by directly optimizing compliance with the target specification with respect to the design parameters. The proposed method is a general framework for computing the generalized inverse of a high-dimensional nonlinear function that does not require, for example, gradient information, which is often unavailable from discipline models. We furthermore develop a three-tier convergence criterion based on: 1) convergence to a solution optimally satisfying all specified design criteria; 2) detection that a design satisfying all criteria is infeasible; or 3) convergence to a probably approximately correct (PAC) solution. We demonstrate the proposed approach on benchmark functions and a vehicle chassis design problem motivated by an industry setting using a state-of-the-art commercial discipline model. We show that the proposed approach is general, scalable, and efficient and that the novel convergence criteria can be implemented straightforwardly based on the existing concepts and subroutines in popular BO software packages.

One of the key challenges in developing a vehicle for excellent vehicle dynamics is being able to achieve a high level of driving comfort without degrading the steering and handling performance.The part of driving comfort discussed in this paper are tactile vibrations up to f = 100 Hz. This paper describes how Multi-Body Dynamics (MBD) Computer Aided Engineering (CAE) tools are applied to optimize such vibrations in the early phase of the development process. The approach hereby presented combines system level testing with MBD for the study of ride comfort, similar to the way that system level kinematics and compliance testing is combined with MBD to support steering and handling investigations. Laboratory investigations have been executed to fully characterize a reference suspension with respect to frequency and amplitude behavior. The respective MBD models have been subsequently refined and validated versus physical laboratory measurements. Several examples for a front wheel drive passenger car will be given, which show how these models can be used to effectively support chassis development in early design phases. The first example demonstrates the effect of subframe isolation on ride comfort via analyses of the forces transmitted to the vehicle body. The second example demonstrates the sensitivity of bushing stiffness to impact harshness using a full vehicle model. The final example demonstrates how impact harshness can be optimized using different levels of compliance split between the subframe and suspension bushings.