Graduate Thesis Or Dissertation
 

Drilling process evaluation by predicting drilled hole quality and drill bit wear with on-line acoustic emission signals

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/9019s578j

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  • Improvement of manufacturing productivity is dependent on the successful automation of manufacturing processes, the success of which is based in turn upon the availability of information which describes the state of manufacturing operations. Acoustic Emission (AE) signals related to the cutting process and tool wear have been recently applied to monitor manufacturing processes, and various AE parameters can be used to provide process information. For example, when cutting tools become worn, AE energy generated at the interface of tool flank and work piece increases. This study is thus an experimental investigation of the AE spectrums representing AE signals energy distribution to determine the possibility of extracting useful parameters to provide on-line information about drilled-hole quality and drill-bit wear. An experiment conducted using a radial-arm drilling machine was employed to collect on-line AE drilling process spectrums, yielding eight indicator parameters. Drill wear states were measured using a machine vision system. Assessment of the drilled hole quality was based on tolerances established in Geometric Dimensioning and Tolerancing (GD&T). Correlations among drill wear, drilled-hole quality measurements, and the AE spectrum indicator parameters were examined by regression analysis. A forward-stepwise variable selection procedure was used to select the best-fit regression model for each drilled hole quality measurement associated with the set of one AE parameter raised to different powers. According to quality measurements, drilled holes were categorized as either "acceptable" or "unacceptable" holes, using cluster analysis with a group-averaging method. The usage of AE parameters to decide to which group a drilled hole belonged was also examined. From the experimental evidence, it was observed that there are strong relationships between AE parameters and drill-wear state and the quality measurements of drilled holes. AE parameters could be useful predictor variables to provide information to controller/operators to evaluate current drilling processes. Based on the status information of drill wear and the quality measurements, drilling processes can be adjusted accordingly.
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