<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/9998907" mdate="2014-07-04 00:00:00"> <author>Bharatendra Rai</author> <title>A Study of Classification Models to Predict DrillBit Breakage Using Degradation Signals</title> <pages>2389 - 2392</pages> <year>2014</year> <volume>8</volume> <number>8</number> <journal>International Journal of Economics and Management Engineering</journal> <ee>https://publications.waset.org/pdf/9998907</ee> <url>https://publications.waset.org/vol/92</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drillbits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drillbit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drillbit breakage using degradation signals. </abstract> <index>Open Science Index 92, 2014</index> </article>