| Summary: | 碩士 === 國立中興大學 === 機械工程學系所 === 103 === The machining performance of the spindle of a milling machine is highly related to its operation condition. Therefore it is important to monitor the operation status of the spindle. While the most important factor affecting spindle operation is the condition of its bearings, this research proposes a diagnostic method to improve accuracy for identifying bearing fault status of the milling spindles via measuring rotation signals of operations and establishing a decision model. The developed method is testified against several spindles from market and shows it can be used to identify the condition of spindle bearings.
In the study, the artificial neural network (ANN) is employed to build the decision model. The failure mode and effect and the feature corresponding to the failure of spindle bearings are analyzed at first. The items and signals to be measured are then designed accordingly. These signals are then collected from a lot of spindles, with some of them being damaged. Conditions and operation signals of bearings, dissembled from these spindles, are also measured. These measured signals are used to train the ANN for building the relationship among the bearing conditions and the measured signals. Two approaches, two-step and one-step approaches, are further conducted to compare the accuracy of the models. The result shows that the accuracies of the two approaches were 85% and 81% respectively but the one-step approach is more practical as it can be employed to industrial application directly. The major contribution of this research is the approach to build up an ANN model that can be used to identify the status of spindle bearings with fairly good accuracy. The model can be further employed for predictive monitoring of the spindle of a machine tool such that failure of the spindle can be forecasted to prevent against the loss of production due to spindle failure.
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