Self-adaptive main shaft performance degradation identification method based on S test piece
A recognition method and self-adaptive technology, applied in the recognition of patterns in signals, character and pattern recognition, testing of mechanical parts, etc., can solve the problems of white noise can not be completely neutralized, noise residual, etc., to achieve reasonable preventive maintenance , the effect of rationally arranging production
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[0093] Different degrees of wear on the cutting edge of the tool are used to simulate different degrees of failure of the spindle. A total of three tool working conditions are set. When milling the S specimen under each working condition, the same processing technology and cutting conditions are used to collect the vibration signals in the radial direction of the spindle, as shown in image 3 shown.
[0094] The material of the S test piece is aviation aluminum 7075-T7451, and it is clamped on the workbench with countersunk screws. The processing tool is a φ20mm three-blade end mill. The first working condition is that two cutting edges have crater wear, the second working condition is that one cutting edge has crater wear, and the third working condition is a brand new milling cutter. The three working conditions respectively simulate the electric spindle bearing with two balls worn out, one ball worn out, and the bearing has no faults.
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