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Investigation of the abrasive flow machining process and development of a monitoring strategy using acoustic emission
Abstract
Finishing operations, which presently account for approximately 15% of the total machining costs, have been identified as the single greatest hurdle remaining in fully automating the production of precision components. A new process called Abrasive Flow Machining (AFM) offers the possibility of effective automation needed by the manufacturing community. AFM is a nontraditional finishing process that is used to deburr, polish, and radius workpieces by flowing an abrasive-laden viscoelastic compound across the surface to be machined. Due to the complex and random nature of AFM, deterministic and stochastic analysis techniques are needed to understand the process and to find a quantitative measure of the metal removal mechanism which can be integrated with an on-line control strategy. Therefore, this dissertation presents research on the following specific topics related to the AFM process: metal removal and surface roughness characteristics per cycle for a single hole part, machining of multiple hole components, prediction of metal removal and flow rate in AFM using an acoustic emission monitoring system, and stochastic modeling and analysis of AFM surface roughness profiles and acoustic emission signal data. In studying the machining characteristics per cycle, the largest change in bore diameter and surface roughness always occurred from the raw workpiece to the first AFM cycle. A better starting surface roughness was associated with a better final roughness value. A velocity distribution of the abrasive-laden media was proposed for a multiple hole component. Experimental results showed that the center hole had, on average, 30% more metal removal as compared to the outer holes. The effect of media temperature on flow rate also helped to clarify the results. An AFM monitoring system using acoustic emission (AE) technology was designed, built and tested. Relationships were proposed between the acoustic emission level and the AFM process parameters. It was shown that metal removal in AFM could be fairly accurately predicted knowing the RMS of the AE signal and the levels of the machining parameters. A high correlation was found between AFM flow rate and AE RMS over certain ranges. Data Dependent Systems (DDS) analysis of the acoustic emission signals revealed distinct frequency bands during AFM that were linked to the process mechanisms. Good agreement was found between DDS frequency decomposition and the results of a spectral analysis option on the new data acquisition system.
Subject Area
Industrial engineering|Mechanical engineering
Recommended Citation
Williams, Robert Edward, "Investigation of the abrasive flow machining process and development of a monitoring strategy using acoustic emission" (1993). ETD collection for University of Nebraska-Lincoln. AAI9333989.
https://digitalcommons.unl.edu/dissertations/AAI9333989