Mr. Hassan, thank you for the presentation! Which machining parameters are updated after optimization step and how are safety of these machined parameters checked?
Dear Mr. Kecibas,
For the specific application of tool chipping prevention, we used a “Feed hold” command instead of an “emergency stop” command in order to stop any feed motion of the spindle without losing the setup parameters. The tool is then retracted and changed by the operator. However, we built a realtime full 2-way communication with the machine control that allow us to read and write the control data on the fly (e.g. speed, feed, axial positions,…). We utilized these parameters to safely optimize the drilling of hybrid stacks in one of our publications in CIRP:
Sadek, A., M. Hassan, and M. H. Attia. “A new cyber-physical adaptive control system for drilling of hybrid stacks.” CIRP Annals 69.1 (2020): 105-108.
Dear Mr. Hassan, thank you for your interesting presentation of senor-based tool condition monitoring. It is impressive to see that you could forecast tool chipping in advance by some 15 ms or even more via effective detection of tool pre-failure crack development. In conjunction with a fast data processing time of max duration 2 ms and a mahine tool stopping time less than 15 ms, this approach appears to be truly a “real-time” monitoring system. I wonder whether, besides tool chipping, you also considered the occurrence of full tool breakage where a totally catastrophic tool failure occurs suddenly. It would be very interesting to be able to detect the onset of pre-breakage phenomena also for this type of catastrophic tool failure.
Dear Prof. Teti,
The same approach (TKEO-HHT + AErms signals) is applicable for tool breakage prevention. This is because the approach is sensing the unstable crack propagation, which precedes tool chipping or breakage. This has been validated but not included in this publication. It predicts the onset of fracture before it happens within the same time window ranges. If you are interested in detecting the onset of fracture itself, the same signal processing approach can be applied to the vibration signals, as shown in the high speed milling example in slide 12 of the presentation.
Mr. Hassan, thank you for the presentation! Which machining parameters are updated after optimization step and how are safety of these machined parameters checked?
Dear Mr. Kecibas,
For the specific application of tool chipping prevention, we used a “Feed hold” command instead of an “emergency stop” command in order to stop any feed motion of the spindle without losing the setup parameters. The tool is then retracted and changed by the operator. However, we built a realtime full 2-way communication with the machine control that allow us to read and write the control data on the fly (e.g. speed, feed, axial positions,…). We utilized these parameters to safely optimize the drilling of hybrid stacks in one of our publications in CIRP:
Sadek, A., M. Hassan, and M. H. Attia. “A new cyber-physical adaptive control system for drilling of hybrid stacks.” CIRP Annals 69.1 (2020): 105-108.
Dear Mr. Hassan, thank you for your interesting presentation of senor-based tool condition monitoring. It is impressive to see that you could forecast tool chipping in advance by some 15 ms or even more via effective detection of tool pre-failure crack development. In conjunction with a fast data processing time of max duration 2 ms and a mahine tool stopping time less than 15 ms, this approach appears to be truly a “real-time” monitoring system. I wonder whether, besides tool chipping, you also considered the occurrence of full tool breakage where a totally catastrophic tool failure occurs suddenly. It would be very interesting to be able to detect the onset of pre-breakage phenomena also for this type of catastrophic tool failure.
Dear Prof. Teti,
The same approach (TKEO-HHT + AErms signals) is applicable for tool breakage prevention. This is because the approach is sensing the unstable crack propagation, which precedes tool chipping or breakage. This has been validated but not included in this publication. It predicts the onset of fracture before it happens within the same time window ranges. If you are interested in detecting the onset of fracture itself, the same signal processing approach can be applied to the vibration signals, as shown in the high speed milling example in slide 12 of the presentation.