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A Study on the Diagnosis of Cutting Tool States Using Cutting Conditions and Cutting Force Parameters (II) ABSTRACT
In this study, statistical and neural network methods were used to recognize the cutting tool states. This
system employed the tool dynamometer and cutting force signals which are processed from the tool
dynamometer sensor using linear discriminent function. To learn the necessary input/output mapping for
turning operation diagnosis, the weights and thresholds of the neural network were adjusted according to
the error back propagation method during off-line training. The cutting conditions, cutting force ratios and
statistical values(standard deviation, coefficient of variation) abtained from the cutting force signals were
used as the inputs to the neural network. Key Words :error back propagation(¿ÀÂ÷¿ªÀüÆÄ), neural network(½Å°æȸ·Î¸Á), cutting condition(Àý»èÁ¶°Ç) cutting force parameter(Àý»è·Â ÆĶó¸ÞŸ) # (4.55MB) ¸¦ InstallÇÑ ÈÄ¿¡ ´ÙÀ½ ÀÚ·á(pdf-file)¸¦ ¹Þ¾Æ º¼ ¼ö ÀÖ½À´Ï´Ù. ³í¹®ÀÇ îïÙþ Download (pdf-file, 571KB, ¿ëÁöA4 6¸Å) |