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Condition monitoring of tools in CNC turning

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posted on 18.03.2014, 15:26 by Brian P. Hede
The metal cutting industry today is highly automated and, as a step towards Europe's ability to compete on the world market, an increased level of automation can be expected in the future. Therefore, much attention has been paid to the use of automated monitoring systems within the maintenance strategies designed to prevent breakdown. This research focuses on the condition monitoring of cutting tools in CNC turning, using airborne acoustic emission, (AAE). A structured approach for overcoming the problems associated with changing cutting parameters is presented with good results. A reverse and novel approach in estimating gradual tool wear in longitudinal roughing has been made by predicting cutting parameters directly from the acoustics emitted from the process. Using the RMS as a representation of the energy in the signal, where the spectral distributions are working as divisional operators, it has been possible to accurately extract a representation of feed rate, depth of cut and cutting speed from the signal. Using a simplified relationship to estimate tangential cutting force, a virtual force can be calculated and related to a certain amount of flank wear using non-linear regression. Furthermore, this research presents a monitoring solution where disturbances are eliminated by recognising the sound signatures where it, afterwards, is possible to evaluate the reliability of the wear decision. This is done by describing irregularities in the signal , where surface parameters used on a sound waveform, combined in a neural network, has been used to trigger outputs for several defined classes of disturbances. An investigation of the two wear types flank and crater wear, has been conducted and is has been concluded, that although crater wear has an effect on the AAE, it is difficult to recognise this. AAE has shown to an efficient tool to detect flank wear, where a direct relationship is shown between the changes in the cutting parameters, tool wear and AAE. This approach has resulted in a precise monitoring so lution, where flank wear can be estimated within an error of I0%.



  • Mechanical, Electrical and Manufacturing Engineering


© Brian Peter Hede

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A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.

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