*3.3. Material Detection*

The online detection of the machined material is needed to realize a material-specific machining of hybrid components. For this reason, the sensitivity of monitoring a material compound based on the material-specific cutting force was investigated. A longitudinal turning process of EN AW-6082 and 20MnCr5 was performed and the signals of the dynamometer and the feeling turret were compared by the statistical overlap factor (SOF). The SOF determines the degree of separation of the material-specific cutting force between the first and second material. If a signal shows a high degree of separation due to different material properties, the SOF increases. In addition to the resulting standard deviation when measuring the cutting force, the influence of the variance of the simulated material removal rate *Qw* was considered. The determined triple standard deviation for the simulation of *Qw* is 3–4%. With *Qw,min*, the value at the lower end of the triple standard deviation is taken into account for the first material *i*. In contrast, *Qw,max* is assumed for the second material *j*. This results in the material-specific cutting force being calculated too low for steel and too high for aluminum in order to obtain the highest possible variation. To guarantee robust monitoring, an SOF greater than six is necessary to separate two materials during machining.

$$SOF\_{ij} = \frac{\left| \frac{F\_{c,i}}{Q\_{w,\min}} - \frac{F\_{c,j}}{Q\_{w,\max}} \right|}{\sigma \left( \frac{F\_{c,i}}{Q\_{w,\min}} \right) + \sigma \left( \frac{F\_{c,j}}{Q\_{w,\max}} \right)} \tag{7}$$

With a constant *ap* of 1 mm, the SOF was calculated for a varying feed rate of *f* = 0.1–0.4 mm and a cutting speed of *vc* = 200–400 m/min, which are depicted in Figure 7. The influence of the simulation error of *Qw* on the calculated SOF can be neglected. For the investigated aluminum-steel compound, the SOF decreases by a maximum of 5% if the highest possible variation of *Qw* is considered instead of assuming a constant value for *Qw*. The significant impact is, therefore, the measuring accuracy and the signal-to-noise ratio of the cutting forces.

**Figure 7.** Material-specific cutting force for varying feed and cutting speed.

For the feeling turret, the sensitivity of the monitoring system improves if the the feed and, thus, the cutting force is increased. This is because the SNR is much more significant in the signal of the structure-integrated strain gauges compared to the signals of the dynamometer. Consequently, the SNR has a larger impact on the measurement of *Fc* and, subsequently, on the quality of the material identification at lower process forces. For a feed of *f* = 0.1 mm, the average measured SOF is 6.0, while, at *f* = 0.4 mm, the average SOF increases to 12.6. With the dynamometer, this effect does not appear due to the lower SNR. However, the SOF also varies for the material-specific cutting force measured by the dynamometer over the examined process parameter range. This is caused by the higher sensitivity to varying chip formation. Di fferent chip forms results in changing peak2peak values of the signal and, consequently, in di fferent standard deviations of the force signal. This e ffect is less significant for strain gauges due to the higher damping of the turret structure. In general, a higher SOF value and, thus, better sensitivity is achieved by using the dynamometer for all considered process parameters. The maximum statistical overlap factor is 23.9 while the minimum SOF is 13.5. Both measuring systems are, therefore, qualified to robustly monitor the material during the machining of the aluminium-steel compound for the examined process parameter range.
