processes described at
processes described at the beginning, quality demands for gears for e-transmissions are extremely high. “The required tolerances of profile angle, flank line angle, concentricity, two-ball dimension are in some cases smaller than in the conventional drive train by a factor of 3. For the flank line angle error fHß, a typical requirement is ± 4 mm; with combustion engine transmissions, this was sometimes ± 13 mm,” says Friedrich Wölfel, describing the requirements of his customers. Together with the required machine and process capabilities, these quality requirements are testing the limits of what is technically and economically feasible. And the static and dynamic stability of the processing machine and process cannot be increased at will. The only way out is to start with the methods of analysis and control. Because otherwise: the narrower the tolerance limits become with the same machine/ process capability, the greater the number of measured components must be. However, this involves a great deal of effort. And ultimately, downstream component testing does not add value either. With regard to the approach to trend-related deviations from the target geometry in particular, the ‘closed loop’ has already established itself as an important tool. This accelerates and improves the feedback between downstream gear measurement and the processing machine itself. Here the results of the inspection on the measuring machine are no longer printed out and made available to the machine operator on paper for evaluation, but are transmitted directly to the processing machine as a standardised file. The grinding machine then uses preselectable tolerance corridors to decide independently whether it needs to intervene in the process at all, for example with scalable correction values. If unexpectedly high variances from the target geometry occur, the decision on how to proceed is then up to the operator themselves (figure 1). The referee at the end of the manufacturing process At the end of the manufacturing process of a complete transmission is what is known as an ‘end-of-line test bench’. It is no longer just individual gears that are tested here with regard to their quality, but fully assembled transmissions are evaluated. They go through various test cycles that simulate operating conditions in an actual vehicle. The operating noise is also recorded. Acousticians can analyse this data to extract intervention ratios, typical frequencies and possible interfering noises. “Unfortunately, this also means that gearing defects are only noticed at the end of the manufacturing process,” complains Friedrich Wölfel. “The complete transmission must then be dismantled, the individual components checked and, based on this, analysed to determine which component is responsible for the anomaly on the test bench. It could also be that a complete batch of components can cause problems. But that only becomes apparent when the entire value chain has already been completed.” Today it is possible to identify components that could cause noise before they are installed in the transmission. A very common procedure for e-drives is the waviness analysis on gear surfaces. Here, profile, line and pitch measurements are carried out on all teeth on the gear measuring machine and lined up in such a way that the gear is mapped over its complete circumference. The waviness on the gear wheel can be mathematically measured. However, starting with the complete measurement of the gears, this procedure is very timeconsuming and thus unsuitable for testing every single piece in series production. Friedrich Wölfel on this: “The grinding time of typical e-transmission components is less than one minute, whereas the measuring time is four to six minutes; indeed, in the case of an all-tooth measurement as the basis of a waviness analysis, it can be significantly more. And ultimately, downstream component testing does not add value either. What is needed here is further development of in-process analysis, which allows conclusions to be drawn about the component quality produced during the machining itself.” Identify possible noise problems at the machining stage A promising approach is indeed to detect possible defects as early as the grinding process. Process monitoring is the buzzword. Achim Stegner explains the approach: “We already have numerous sensors and measuring systems in the machine that can provide us with many indications, measured values and information. At the moment, we primarily use it only to operate the functions of the machine. In the future, however, we also want to use it to assess the machining process directly in the machine.” figure 2 Error analysis and index calculation in the machining process 32 no. 1, March 2022
processes However, this does not mean integrating an additional tactile measuring function into the grinding machine in order to achieve a faster closed loop. Nor is it a question here of inspecting a ground component directly in the machine, evaluating it and correcting any discrepancies during the production of further components. The focus is rather on analysing the machining process in real time (!) in order to detect deviations from a previously defined reference process. However, it is not enough to only define envelopes for signals from the machine to do this. This can be explained using the “power consumption of the grinding spindle” signal in figure 2 as an example. This signal can be used to detect a possible flank line angle error (fHß) at an early stage. Stegner: “However, the procedure via envelope detection reaches its limits here, as the error is difficult to iden - tify. As long as the signal remains within the envelope, no alarm gets triggered. So you need a more intelligent form of evaluation. An artificial intelligence that attempts to emulate human decision-making structures. This involves making decisions based on a multitude of different information – overlaid with the person’s own experiences – upon which they act. Process monitoring: intervene before it’s too late Process monitoring can be defined as component-specific monitoring and evaluation of the grinding process. As described, it is not a trivial matter to generate an action instruction from the sensor signals. But it is possible. Various characteristic values can be formed from time signals. In the simplest case, these can be maximum or RMS (root mean square) values of the signals. The characteristic values are then combined with the known project data via algorithms and processed into indices, for example a noise or screw breakout index. Achim Stegner explains about trans - mission noises specifically: “An order analysis similar to the order spectrum on an end-of-line test rig can be created for noise-critical components via an FFT (fast fourier transformation). This makes it easier to classify the recorded signals and relate them to results on the transmission test bench (see figure 3). figure 3 Order spectrum, recorded on a transmission test bench Measurement data that is not processed is of no use.” In the end, especially in the manufacturing environment, only appropriate indices help to identify errors very specifically. The benefits of process monitoring can therefore be seen in the following points: → 100 % testing of all components → identification of anomalies while still in the grinding process → detection of component-specific faults → targeted reporting of irregularities → adaptive intervention in the process → parts tracing Next step: standardisation Process monitoring is not yet an app that you can simply download and use. Rather, it is a customer- and application-specific development that defines and monitors indices in relation to the respective component. But even this first step is far more than was considered feasible until only recently. Achim Stegner on this: “Several pilot customers are already using this functionality today. We are already able to detect different errors and intervene on the process side. In addition, we are already working on having the grinding machine teach itself characteristic values for new components. However, this of course requires broad empirical knowledge from error patterns, the geometric quality of the components and corresponding feedback from the transmission test bench.” Friedrich Wölfel adds: “The next goal is that the user can also use this functionality without our component-specific support. It is also important to understand that process monitoring and closed loop are not contradictory, but complementary.” Both approaches to process-integrated quality assurance are already available for Kapp Niles machines today and are continuously being given further functional scopes and utilisation options through the experience gained from series production. further information: www.kapp-niles.com no. 1, March 2022 33
