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Predicting the vibroacoustic quality of steering gears

Paul Alexandru Bucur(pabucur***at***edu.aau.at)
Klaus Frick(klaus.frick***at***ntb.ch)
Philipp Hungerländer(philipp.hungerlaender***at***edu.aau.at)

Abstract: In the daily operations of ThyssenKrupp Presta AG, ball nut assemblies (BNA) undergo a vibroacoustical quality test and are binary classified based on their order spectra. In this work we formulate a multiple change point problem and derive optimal quality intervals and thresholds for the order spectra that minimize the number of incorrectly classified BNA. We pursue a multiobjective goal: the first objective function maximizes the Cohen Kappa metric, while the second objective function reduces the number of employed order intervals. The proposed approach is based on a genetic algorithm and incorporates prior information on the correlation structure of BNA and steering gear vibroacoustics, gained via canonical correlation analysis. The computational experiments show a reduction of both the number of employed order intervals and the costs arising from falsely classified BNA parts with respect to the current production setting, ensuring thus a high practical relevance of our suggested approach.

Keywords: Ball nut assemblies, vibroacoustical quality test, multiple change point problem, genetic algorithm

Category 1: Applications -- Science and Engineering (Data-Mining )

Category 2: Applications -- Science and Engineering (Statistics )

Category 3: Applications -- Science and Engineering (Mechanical Engineering )

Citation: Technical report, Alpen-Adria Universität Klagenfurt, Mathematics, Optimization Group, TR-AAUK-M-O-14-07-18, 2018.

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Entry Submitted: 07/14/2018
Entry Accepted: 07/14/2018
Entry Last Modified: 07/14/2018

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