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A New Tool Wear Monitoring Method Based on Project Pursuit Regression

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Tool wear prediction is a major contributor to the dimensional errors of a workpiece in precision machining, which plays an important role in industry for higher productivity and product quality. Tool wear monitoring is an effective way to predict the tool wear loss in milling process. In this paper, different milling conditions are estimated as the input variables, tool wear loss is estimated as the output variable, and projection pursuit regression (PPR) method is proposed to establish the relationship between the input and the output variables. A real-time tool wear loss estimator is developed based on the PPR model, and experiments have been conducted for measuring tool wear based on the estimator in a milling machine. The experimental and estimated results are found to be in satisfactory agreement with average error lower than 10%.
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Document Type: Research Article

Publication date: 2012-07-01

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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