A study to determine the onset of catastrophic wear of a processing tool by statistical parameters of acoustic emission - Sergii Filonenko (Eastern-European Journal of Enterprise Technologies, 2019)
In the present study, experimental research was carried out to determine the effect of the processing tool wear on the mutual change in the average statistical amplitude parameters of acoustic emission signals. Acoustic emission signals were recorded when turning silumin. The research was aimed at finding parameters to determine the occurrence of critical wear of the processing tool. It is shown that the recorded signals are continuous. The statistical amplitude parameters of the acoustic emission signals were processed under normal and catastrophic wear of the processing tool. It has been determined that the development of normal and catastrophic wear of the tool leads to a decrease in the values of the statistical amplitude parameters of the recorded signals. However, no peculiarities were found in their change to determine the developing type of wear. Meanwhile, at different time intervals, there was an anticipating increase or decrease in one of the statistical amplitude parameters of the acoustic emission signals. The processed parameter was a coefficient characterising a mutual change in the statistical amplitude values of the recorded signals. It has been determined that absence of the tool wear is characterised by stable values of the calculated coefficient in time. The occurrence and development of catastrophic wear leads to an outburst of the value of the calculated coefficient with its subsequent accelerated decrease to the destruction of the tool. The emergence and development of normal wear leads to an increase in the value of the calculated coefficient with a subsequent transition to a sawtooth change and a gradual decrease of its size. The obtained regularities can be used when devising methods for controlling and monitoring the condition of the tool during the mechanical processing of materials, including monitoring the state of the processing tool in robotic industries