Use of Acoustic Emissions to detect change in contact mechanisms caused by tool wear in abrasive belt grinding process

Abrasive belt tools are widely used for finishing processes, where the abrasive grains on the belt tool serve as the cutting edge to remove materials. The interaction between abrasive grain and the material surface might result in three contact mechanisms, i.e. rubbing, ploughing and cutting, where...

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Main Authors: Pandiyan, Vigneashwara, Tjahjowidodo, Tegoeh
其他作者: School of Mechanical and Aerospace Engineering
格式: Article
語言:English
出版: 2019
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在線閱讀:https://hdl.handle.net/10356/90319
http://hdl.handle.net/10220/49948
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機構: Nanyang Technological University
語言: English
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總結:Abrasive belt tools are widely used for finishing processes, where the abrasive grains on the belt tool serve as the cutting edge to remove materials. The interaction between abrasive grain and the material surface might result in three contact mechanisms, i.e. rubbing, ploughing and cutting, where their nature are not fully understood. On the other hand, the performance of a coated abrasive belt tool is highly affected by the grain wear. A single grain scratch test with different abrasive grain wear conditions is conducted to explore the three contact mechanisms. Through scratch experiments of prismatic Aluminium Oxide (A12O3) grain on Aluminium 6061 workpiece, Acoustic Emission (AE) frequency signatures that correspond to the three mechanisms are examined. Dominant frequencies and energy signatures occupied by the three contact mechanisms are analysed using Short-Time Fourier Transform (STFT). The energy content of the dominant frequency signatures revealed that the cutting mechanism is more predominant on belt tool with new grains, which gradually becomes less significant as the grain wears. A similar trend is also observed in ploughing and rubbing modes with respect to the wear flat level of the belt tool. The general conclusion suggests that the intensity of contact mechanisms changes according to the condition of the abrasive grain, i.e. tool wear, and can be correlated with AE sensor data.