Acoustic image-based damage identification of oxide aluminum grinding wheel during the dressing operation

Abstract

Grinding is a finish process of parts that require high precision and tight dimensional tolerance, which owe high value-added. As the grinding process takes place, the cutting surface of the grinding wheel undergoes wear and then its cutting capacity is reduced. On the other hand, the dressing operation is responsible for restoring the cutting surface of the grinding wheel and, therefore, plays a key role in the grinding process. This work aims at obtaining acoustic images of the grinding wheel surface to identify its conditions during the dressing operation. Experimental tests were conducted with a single-point diamond dresser in a surface grinding machine, which was equipped with an oxide aluminum grinding wheel in which specific marks were intentionally made on its surface to simulate damages for identification. An acoustic emission sensor was fixed to the dresser holder and the signal were acquired at 5 MHz. The signal spectrum was investigated and a frequency band was carefully selected, which represented the conditions of grinding wheel surface. The root mean square values were then computed from the raw signal with and without filtering for several integration periods, and the acoustic images obtained. The results show that the proposed technique is efficient to identify the damage on the wheel surface during the dressing operation as well as its location.