Grinding operations can be analyzed through monitoring and analysis of the spindle power during the process. Due to the complexity of the process, the analysis on grinding processing signal still heavily relies on personal experience of the engineer instead of having a standard structured method. Therefore, subjectivity and inconsistency is introduced into the analysis procedure. In this thesis, a general method is established to characterize signal, utilize the characterization result to predict the real time condition of grinding wheels and the impact on the process performance measures, and provide suggestions in modification of process parameters to improve the grinding operation. This method is initiated from signal acquisition and conducted based on characterizing the signal and organizing expert knowledge. When the standard procedure to analyze the grinding process through power signal is established, the correlation between input and output can be understood, which can later be utilized for diagnostic applications. During the diagnosis, the real-time grinding wheel status is estimated and the output of the process is predicted. Then, suggestions on modifying the input parameters to address given output issue are generated. Therefore, a signal analysis and knowledge based monitoring and diagnosing system is developed to help enhance the current grinding process planning. This system is realized with a software tool developed with specifically designed algorithms under Matlab environment, upgrading from manual signal processing to an automated characterization procedure and providing process evaluation and improvement suggestions, which will improve the objectivity, consistency and accuracy in the analysis of grinding processes.
| Identifer | oai:union.ndltd.org:wpi.edu/oai:digitalcommons.wpi.edu:etd-theses-1316 |
| Date | 26 April 2009 |
| Creators | Tian, Wei |
| Contributors | Yiming (Kevin) Rong, Advisor, , , Zhikun Hou |
| Publisher | Digital WPI |
| Source Sets | Worcester Polytechnic Institute |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses (All Theses, All Years) |
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