Automatic system for thermal damage detection in manufacturing process with internet monitoring

Abstract

This work involved the development of a smart system dedicated to surface burning detection in the grinding process through constant monitoring of the process by acoustic emission and electrical power signals. A program in Visual Basic® for Windows® was developed, which collects the signals through an analog-digital converter and further processes them using burning detection algorithms already known. Three other parameters are proposed here and a comparative study carried out. When burning occurs, the newly developed software program sends a control signal warning the operator or interrupting the process, and delivers process information via the Internet. Parallel to this, the user can also interfere in the process via Internet, changing parameters and/or monitoring the grinding process. The findings of a comparative study of the various parameters are also discussed here

Monitoring of Grinding Burn by Acoustic Emission

“…The inputs of the models are obtained from the digital processing of the raw acoustic emission and cutting power signals. The parameters to be obtained and used in this work include the mean-value deviance (MVD), which proved efficient in grinding burn detection (Wang et al, 2001), grinding power, and root mean square (RMS) of the acoustic emission signal (Dotto et al, 2006).…”

“…The percentage of burn and the degree of burn of each workpiece were determined using a software previously developed (Dotto et al, 2006). This software analyzes the surface condition of a workpiece, based on a photograph of the machined workpiece.…”

Digital Signal Processing for Acoustic Emission

“…The DPKS statistic was developed by Dotto [18] in order to increase the sensitivity of the DPO parameter. This parameter allows to identify the exact moment when grinding burn begins, and in the case of dressing, the exact moment to stop the process.…”

Dressing monitoring with a digital signal processor

“…Modifying the expression of the DPO parameter in order to achieve greater sensitivity, the DPKS parameter was developed, which is given by multiplying the standard deviation of AE rms by the sum of the difference between the instantaneous value of the mean electrical power and the standard deviation of this power raised to the 4 th power [7].…”