Assessment of the dynamical properties in EDM process—detecting deterministic nonlinearity of EDM process

Abstract

Time series of gap state were often used as feedback signal in electrical discharge machining (EDM) adaptive control systems. However, models precisely describing the EDM process have never been built because of the once believed stochastic nature of the EDM process. In this case, the power of adaptive controls in EDM had not been fully brought into play. Before building a feasible model, it is prerequisite to determine whether an efficient stable EDM process is nonlinear or linear, deterministic or stochastic. The main purpose of this paper is to investigate the deterministic nonlinearity of the process. A discriminating method was first provided to judge states in the gap at sampling intervals from voltage and current. Gap state was then statistically quantified from a train of discriminated states at sampling intervals within a specified period of time. Based on a time series of gap state data, we took use of surrogate data method to detect the nonlinearity of the process. From the results of two kinds of tests, it can be concluded that the deterministic nonlinearity of the process reflected by gap states is intrinsic.