Post by account_disabled on Feb 19, 2024 17:27:49 GMT -10
As an example of the results obtained, an analysis of currents and sound is presented below. Figure 3 shows how the maximum current corresponding to blades 3 and 4 evolves as each of the crankshafts is processed. At the beginning of the life of the tool, the two blades have a maximum current of the same order, however, as the number of worn parts increases, a difference is observed and while the maximum current of blade 3 decreases, that of blade 4 increases. . This behavior is due to non-uniform wear of the blades, thus blade 3 suffers greater wear than blade 4, so it has to exert greater effort to remove the chip. For this reason its maximum current increases. Likewise, when the tool is replaced, the maximum currents return to their initial value.
Below, Figure 4 shows the behavior of the acoustic signal through the evolution during a machining cycle, of the RMS value calculated between the Asia Mobile Number List frequency bands from 5728 to 14000 Hz. This figure represents zones A and B. of machining indicated in figure 2. In addition, for comparison, the signal is represented jointly in the case that the tool machines its first part and after it has machined part No. 622. The peaks of the graph correspond to each of blade attacks. The non-uniform wear of each of them can also be observed. Figure 4 – RMS evolution of acoustic signal Conclusions After the analysis of the experimental data collected, it has been observed that tool wear can be monitored through the proposed multivariate analysis.
Likewise, the analyzes in the time and frequency domain are complementary, being indicated for different types of failures. Analysis in the frequency domain is more suitable for monitoring tool wear, while analysis in the time domain is more sensitive to failures related to tool breakage and tool misalignment. The online supervision of the tool requires a simplification of the calculated parameters due to the large amount of data handled in each of the cycles. This is largely due to the processing speed required and the large storage capacity required to collect all of the signals presented.
Below, Figure 4 shows the behavior of the acoustic signal through the evolution during a machining cycle, of the RMS value calculated between the Asia Mobile Number List frequency bands from 5728 to 14000 Hz. This figure represents zones A and B. of machining indicated in figure 2. In addition, for comparison, the signal is represented jointly in the case that the tool machines its first part and after it has machined part No. 622. The peaks of the graph correspond to each of blade attacks. The non-uniform wear of each of them can also be observed. Figure 4 – RMS evolution of acoustic signal Conclusions After the analysis of the experimental data collected, it has been observed that tool wear can be monitored through the proposed multivariate analysis.
Likewise, the analyzes in the time and frequency domain are complementary, being indicated for different types of failures. Analysis in the frequency domain is more suitable for monitoring tool wear, while analysis in the time domain is more sensitive to failures related to tool breakage and tool misalignment. The online supervision of the tool requires a simplification of the calculated parameters due to the large amount of data handled in each of the cycles. This is largely due to the processing speed required and the large storage capacity required to collect all of the signals presented.