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Methodology of Preparing Hypoid Gears for Vibroacoustic Diagnostics in Laboratory Conditions

Płocica M., Marciniec A.

Machine Dynamics Research

2017

Vol. 41, No. 3

63--72

This article presents the methodology of preparing a hypoid gear for vibroacoustic diagnostics with consideration to signal recording conditions and ensuring peripheral parts’ noise reduction. The procedure is aimed at increasing the clarity of the signal generated by meshing for its more precise and unambiguous interpretation.

Cognitive failure cluster enhancing the efficiency and the precision of the self-optimizing process model for bevel gear contact patterns

Schmitt R., Niggemann C., Laass M.

Journal of Machine Engineering

2012

Vol. 12, No. 1

55--65

The contact patterns of bevel gear sets are an important indicator for the acoustic quality of rear axle drives. The contact patterns are the result of complex interactions in the production process. This is due to many process steps, numerous influencing factors and interdependencies. In general, their effect on product variations is not fully comprehended. This impedes the design and control of the production process based on a holistic analytical model for new variants fulfilling the acoustic requirements. The approach with self-optimization is possible but can take a long time for the training of the artificial neural networks and the necessary iterations until a satisfying precision for the predicted process parameters is achieved. Also it can occur that the algorithm is not converging and therefore no satisfactory result is turned out at all. In this paper an approach is presented combining the flexibility of self-optimizing systems with the higher precision of delimited solution finders called the Cognitive Failure Cluster (CFC). The improvements provided by the clustering of the optimization program are evaluated regarding the training time and the precision of the result for a production lot of bevel gear sets.