Browse Publications Technical Papers 2001-01-1505

Gear Whine Improvements for an Automatic Transmission through Design Retargeting and Manufacturing Variability Reduction 2001-01-1505

Gear whine in 1st gear for an automatic transmission that has been in production for nearly thirty years was identified as an NVH issue. Due to advances in vehicle level refinement, and reduction of other masking noises, the automatic transmission gear whine became an issue with the customer. Since the transmission was already in production, the improvements had to be within the boundaries of manufacturing feasibility with existing equipment to avoid costly and time consuming investment in new machines. The approach used was one of identifying optimum values of existing gear parameters to provide a reduction in passenger compartment noise.
The problem was in a light truck application. Objective noise measurements were recorded for 10 transmissions from more than 50 driven in vehicles. The transmissions were disassembled and the gears inspected. Based on statistical analysis of the data, and using a CAE model with input from this measured data, a set of gear dimensions were identified for retargeting. This was not a complete redesign, but rather a fine-tuning of some gear dimensions with no changes to the manufacturing process. Parts made to this new specification showed a significant reduction in gear whine levels.
On analyzing the data that was collected for these gear dimensions, it was noted that the variability of these dimensions in high volume manufacturing was more than desired for optimum noise performance. Since the design retargeting was in the order of a few microns, there existed the probability there would be noisy gearboxes in the hands of the customer because of the variability. A statistical study was undertaken in the gear manufacturing line to identify the sources of variability and establish a way to control these critical dimensions in high volume production within the specified target range. The recommendations from this study are currently being implemented in the manufacturing plant.


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