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Flexible Automation and Integrated Manufacturing 1998

ISBN Druckformat: 978-1-56700-118-1



This paper presents the development of an intelligent predictive tool for improving the accuracy of parts produced on CNC turning centers. Actual dimensions and shape (form) produced on components during machining, differ from the nominal dimensions commanded in the CNC program. These dimensional and form errors on parts depends on a variety of factors such as tool/work deflections, machine tool accuracy, machining conditions, etc. These errors, if predicted, can be used to correct the ideal CNC code and thus improve the part accuracy in CNC machining.
In the present work Artificial Neural Networks (ANN) are used to capture the complex relationship between the errors on the component and the input process conditions. Significant improvement in part accuracy has been obtained using the corrected CNC code during machining.
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