document: Support vector machines model for classification of thermal error in machine tools This paper addresses a change in the concept of machine tool thermal error prediction which has been hitherto carried out by directly mapping them with the temperature of critical elements on the machine . The model developed herein using support vector machines , a powerful data-training algorithm , seeks to account for the impact of specific operating conditions , in addition to temperature variation , on the effective prediction of thermal errors . Several experiments were conducted to study the error pattern , which was found to change significantly with variation in operating conditions . This model attempts to classify the error based on operating conditions . Once classified , the error is then predicted based on the temperature states . This paper also briefly describes the concept of the implementation of such a comprehensive model along with an on-line error assessment and calibration system in a PC-based open-architecture controller environment , so that it could be employed in regular production for the purpose of periodic calibration of machine tools
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