WEAR STATE DISCONTINUOUS TRACKING MODEL AS TWO-LAYER PERCEPTRON WITH NONLINEAR TRANSFER FUNCTIONS BEING TRAINED ON AN EXTENDED GENERAL TOTALITY REGARDING STATISTICAL DATA INACCURACIES AND SHIFTS

Abstract

There is presented a framework for tracking metal tool wear states discontinuously, when the states’ finite set has been statistically tied to the set of representative wear influencing factors. Range of wear states is presumed to be wholly sampled into those factors. The tracker is two-layer perceptron with nonlinear transfer functions. It is a static model, unlike evolutionary dynamic models of forecasting wear. Its identification starts with forming the initial finite general totality con-taining correspondence between influencing factors and each known wear state. Two-layer perceptron is then trained on an extended general totality, whose elements are sum of pure representatives and normal variates’ values in two terms. The first term models jitter inaccuracies and omissions in statistical data or measurements. The second term models possible shifts of wear influencing factors’ values in every state. The identification final stage is the input of two-layer perceptron is re-fed with the pure representatives for making sure that they have not been disassociated from the initially given wear states. It is said also about liable and easy realizability of the tracking model. When range of wear states embraces all practiced wears, the presented two-layer perceptron tracker will control metal tool object wear states with minimized error, ensuring negligi-bility of underuse or overuse of materials.