TED-AJ03-428 DETECTION OF THERMAL DAMAGE IN GRINDING BY ACOUSTIC EMISSION SIGNAL

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In recent years, acoustic emission (AE) instruments and systems have been developed for the monitoring and nondestructive testing of the structural integrity and general quality of a variety of materials, manufacturing processes, and some important devices. Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. Many statistics have shown effective to detect burn, such as the root mean square (RMS), correlation of the AE, constant false alarm (CFAR), ratio of power (ROP) and mean-value deviance (MVD). However, the CFAR, ROP, Kurtosis and correlation of the AE have been presented more sensitive than the RMS as shown in Fig. 1 Among these statistics, it can be clearly seen in Fig. 1 that the ROP underwent more variation in the occurrence of thermal damage on the workpiece surface than did the others. On the other hand, this statistic did not work well for the VC131 steel. Also, Kurtosis had good variation but it did not work to detect burn for any condition tested as neither did skew. Notwithstanding, the ROP, Correlation and Kurtosis could be used with other process signal, like cutting power, providing a good parameter to burn. Thus, this study concludes that CFAR is the best statistic for thermal damage detection in the grinding process investigated. So, it could be implemented into a digital signal processing (DSP) chip and, in turn, providing the analog signal to control the grinding process online. [figure]
一般社団法人日本機械学会の論文

TED-AJ03-428 DETECTION OF THERMAL DAMAGE IN GRINDING BY ACOUSTIC EMISSION SIGNAL

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