An improved Canny operator based on the method of Maximum Classes Square Error is adopted to get a self-adaptive threshold for grain recognition. First, a grinding wheel surface was measured by using a vertical scanning white light interferometric (WLI) system and reconstructed with an improved centroid algorithm; then the grains were extracted using the proposed method based on the fact that the peak intensity difference (ΔI) between maximum and minimum intensities on interferometric curve from diamond is larger than that from bond due to different reflective characteristics of different materials; third the grain protrusion parameters are investigated for grinding performance analysis. The experiments proved that the proposed grain recognition method is effective and assessment parameters are useful for understanding grinding performance.
The topograpgy characterization of grinding wheel grain is indispensable for precision grinding, it depends on accurate edge detecting and recognition of abrasive grains from wheel bond to a large extent. Due to different reflective characteristics arising among different materials, difference between maximum and minimum intensity (Δ ) of diamond is larger than that of bond. This paper uses a new method for grain edge detection of resin-bonded diamond grinding wheel that combines the improved Canny operator in Method of Maximum Classes Square Error (called as OTSU) with ΔI obtained by the white light interferometry (WLI). The experimental results show that the method based on improved Canny operator can effectively detect the edge of diamond grain.