Tool-Wear-Estimation System in Milling Using Multi-View CNN Based on Reflected Infrared Images

초록

A novel method for tool wear estimation in milling using infrared (IR) laser vision and a deep-learning algorithm is proposed and demonstrated. The measurement device employs an IR line laser to irradiate the tool focal point at angles of -7.5 degrees, 0.0 degrees, and +7.5 degrees to the vertical plane, and three cameras are placed at 45 degrees intervals around the tool to collect the reflected IR light at different locations. For the processing materials and methods, a dry processing method was applied to a 100 mm x 100 mm x 40 mm SDK-11 workpiece through end milling and downward cutting using a TH308 insert. This device uses the diffused light reflected off the surface of a rotating tool roughened by flank wear, and a polarization filter is considered. As the measured tool wear images exhibit a low dynamic range of exposure, high dynamic range (HDR) images are obtained using an exposure fusion method. Finally, tool wear is estimated from the images using a multi-view convolutional neural network. As shown in the results of the estimated tool wear, a mean absolute error (MAE) of prediction error calculated was to be 9.5 similar to 35.21 mu m. The proposed method can improve machining efficiency by reducing the downtime for tool wear measurement and by increasing tool life utilization.

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