On the hunt for vibrations: artificial neural networks for quieter steering gears

Engineering | Smart factory | trends of technology | Ball screws have a big impact on the acoustic behavior of steering gears. thyssenkrupp has developed a new process that can determine their quality much more precisely than before. Artificial neural networks identify complex connections as part of this process

When talking about the acoustic experience of driving, the first thing most people think of is engine noise. But there is another element that has a major impact on the driving experience – the steering gear, which has a significant influence on background noise in the vehicle. One reason for this is that the steering gear transmits the movements of the wheels into the interior of the vehicle through vibrations. But noises that are deemed unpleasant are supposed to be as quiet as possible, whereas warning signals have to be heard by the driver.

As a system within the steering gear, the ball screw, in combination with the rack and pinion, ensures that the steering movement is supported: If you look along the rack, you can see the inner circle of the ball screw with the small balls

Inside the steering gear, the ball screw, in combination with the rack and pinion, ensures that the steering movement is supported: If you look along the rack, you can see the the small balls of the ball screw

So in the production process, completed steering gears are subjected to extensive final inspections, during which their acoustic properties are measured precisely. However, these gears are made up of dozens of moving and rigid parts, many of which impact the overall quality. One that is especially important here is the ball screw. It transmits the steering movement to the axle like a worm gear and makes a significant contribution to the vibro-acoustic properties of the whole system.

Steering gear acoustics: No crystal ball for road behaviour

The properties of the ball screw are therefore examined first during the production process. It is rotated on a test stand and the resulting vibration frequencies measured. If their amplitude within specific areas of the observed spectrum exceeds the limits set by acoustics experts, the ball screw is classed as a reject. If, on the other hand, it passes the test, it can be fitted in the steering gear – which should then, ideally, also get through the acoustic test without a hitch.

State-of-the-art production methods: However precise the manufacturing process has been, every ball screw has to be tested for quality at the end

State-of-the-art production methods: However precise the manufacturing process has been, every ball screw has to be tested for quality at the end

But in practice this is not so simple, as a ball screw that tests “OK” can lead to a steering gear that fails the test. There are several reasons for this. First, it is hard to establish a correlation between the quality of the individual components and the steering gear as a whole, meaning that statistical methods have to be used for this. Moreover, the limits for the amplitude of the vibration frequencies are ascertained subjectively through test drives using pre-production components. And last, the final inspection is performed on the assumption that if a single acoustic limit is exceeded just once, this points to a substandard component. However, this can lead to “false rejects.”

AI support: The artificial neural network listens

For this reason, the experts at thyssenkrupp in Eschen, Liechtenstein, have developed a process whereby an artificial neural network is trained to identify common patterns in the vibro-acoustic behavior of ball screws and steering gears and thus arrive at more reliable test results.

An artificial neural network consists of innumerable information processing units that are connected via levels in a network. The units are called neurons – based on the human brain. Neural networks are especially good at identifying patterns – for example, in acoustic signals

An artificial neural network consists of innumerable information processing units that are connected via levels in a network. The units are called neurons – based on the human brain. Neural networks are especially good at identifying patterns – for example, in acoustic signals

This process examines not just individual parts of the frequency spectrum but all of them. In this way, the network is able to identify complex connections between the properties of the components. The insights gained as a result of this then help in testing the ball screw first, as usual, so as to draw conclusions about the quality of the subsequent steering gear.

Neural network leads to new insights into product quality

The new process has proven successful. The artificial neural network has been able to detect data that were not covered by the previous quality measurement procedure. It is these very data that have also yielded information about the quality of the ball screw, and this has enabled the focus on the test stand to be adjusted accordingly. The results of the learning process in the network were not abstract patterns but could be traced back to physical effects in the production process.

Popular product: thyssenkrupp produceshuge quantities of ball screws at Schönebeck

Popular product: thyssenkrupp produceshuge quantities of ball screws at Schönebeck

The new process is also very important to thyssenkrupp in business terms, for the company produces large numbers of ball screws every year at its Schönebeck site. From there, they are distributed to other sites in Europe and all over the world, where they are fitted in steering gears. lt takes two to three days to transport them within Europe and six weeks to deliver them to Central America and Asia. This is why it is especially important that these components should not cause any problems when they are finally installed in the steering gears.

Test pilot makes steering gear more reliable and less expensive

The artificial neural network is still at the trial stage, but its positive impact can already be seen. Its use has led to greater reliability in the assessment of ball screws and, consequently, of the entire steering gear system. This has resulted in a significant cost saving. So it is no surprise that other areas are also running projects using artificial neural networks, for their greater predictive power means artificial neural networks can also be used, for example, to shorten the testing period for final inspections in industrial production.

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