An application of learning machine methods in prediction of wear rate of wear resistant casting parts
In this paper, a method of floating ball wear rate identification, using two machine-learning techniques Support Vector Machine (SVM) and Improved Support Vector Machine (ISVM) are proposed. Both models are used to relate the wear rate and technological parameters of the wear resistant drip moulding using different kernel functions. The models for determining the wear rate of white iron casting with low chromium content (flotation balls), was trained and tested by using the existing exploitation data from the Bar Flotation Plant, Serbia. In order to select the best model parameters the statistical indicators for both models are presented. Results show that the ERBF (SVM) and ERBF+POLY (ISVM) achieved the best classification accuracy compare to other kernels used: the absolute mean error of ERB (SVM) is 5.85%, while the error of ERBF+POLY (ISVM) is 6.67%. The tuned ISVM model with mixture of kernels is able to accurately predict the wear rate and can be used to define the optimum chromium content in liquid metal alloys for the casting of flotation balls. (C) 2013 Elsevier Ltd. All rights reserved.
support vector machine kernel functions optimum chromium content wear rate prediction