Reducing the noise of the braking unit

CONTINENTAL AUTOMOTIVE CZECH REPUBLIC is a major Tier-1 supplier for the automotive industry. To maintain the top quality of its products, the company incorporates progressive methods of testing and analysis of measured characteristics.

Challenge

The vast majority of modern passenger cars are equipped with an electronic parking brake allowing the use of hill assist control and similar technologies. The braking unit consists of the electric motor and the worm drive gearbox. The manufacturing of gears is crucial for the efficiency of the drive, which also includes the noise generated by the operation of the parking brake. The goal is of course to minimize the unwanted noise from applying/releasing the parking brake.

From the engineering experience, it is obvious that the noise level depends on the final manufacturing of gears in the braking unit. However, first, the main source has to be identified in the set of gear geometry characteristics. Then, a proper combination of manufacturing tolerances needs to be found to reduce the noise at optimal costs. The lengthy process of precise measurement of the gearing is limiting the number of collected data available for analysis.

Fig. 1. Electronic parking brake control

Solution

At the start, a review of measured data can give first valuable information about the solved task. The Uptimai preprocessing tool allows the visualization of data points in a variety of adjustable plots. Here, it discovered phenomena such as clustering of measurements or certain types of input variable correlations. Moreover, it helped to identify outliers, from the point of view of input parameter ranges as well as in terms of output values. Also, the data review served to set the method of the Uptimai algorithm correctly, since it had to build a reliable mathematical model from only 50 measured samples when considering 16 input variables**!** The algorithm was able to achieve the overall precision of the model by approximately 90%.

Fig. 2. Review of measured source data

When the surrogate model of the problem was ready, the approach to its analysis was done in the Uptimai postprocessing tool the standard way. Step-by-step it was investigated in all available features of the software just like models that originate not from measured data, but are based on the uncertainty quantification approach coupled with simulation software. Some parameters of the main-gear geometry were identified as the most crucial for the noise. The important finding was that interactions between these parameters cannot be omitted and have to be considered when solving the noise issue.

The model was able to describe the main trends in dependencies between examined outputs and geometric characteristics of the gear, a.k.a. input variables. Based on these, Regions of minimum/maximum were computed for each variable. On each plot, there can be seen immediately where a design point is located in the range of an input variable, where the probability of the increase in unwanted noise is higher (green area) and where the chance for lower noise of the gear prevails (red area). These plots were generated for each output in the analysis and with the simple overlay of such plots engineers were able to find viable trade-offs in tolerance fields of all geometric characteristics.

Fig. 3. Review of measured source data

Speaking about trade-offs, there is also a possibility to directly compare model results of different outputs against each other. Here it was confirmed that noise levels with- and without loading of the gear mechanism are tightly correlated. Moreover, clear trends in behaviour based on the most important inputs were also observed from these plots, revealing the teeth geometry has the same effect in both modes of operation of the gear unit.

Fig. 4. Comparison of noise levels with- and without the load of the gear

Benefits

• Reliable modelling despite the low number of measurements. Models of the noise level of 16 input variables were built from only 50 data samples and achieved approx. 90% of overall reliability.
• The main source of the noise in the electronic brake was identified. The right focus on sources of noise was the first step to successful design.
• Design suggestions were made to reduce the noise level generated by the braking system. Optimization of multiple outputs allowed to improve both modes of braking.