Artificial intelligence for adaptive machining control of surface finish

Abstract

A method of using a mathematical model to adaptively control surface roughness when machining a series of workpieces or segments by: (a) linearizing a geometrical surface roughness model; (b) initializing said model essentially as a function of feed; and (c) subjecting the initialized model to computerized estimation based on roughness and feed values taken from the last machined workpiece, thereby to determine the largest allowable feed for attaining a desired surface roughness in subsequently machined workpieces or segments of the series. The mathematical model is an algorithm of the form R=[1262.79]f.sup.2 /r. The model is linearized and initialized to give the form R=.beta..sub.1 s.sub.R f+.beta..sub.2 R.sub.max where R is the actual roughness and R.sub.max is desired roughness, f is actual feed, .beta..sub.1 and .beta..sub.2 are coefficients to be updated by estimation, and s.sub.R is a scale factor chosen to make the first term have the same order of magnitude as the second term. Computerized estimation is carried out by converting the above initialized linear model to vector matrix notation with provision for the estimated coefficients in the form R.sub.n =.theta..sup.T x.sub.n, where R.sub.n is measured roughness, x.sub.n is a computed vector taken from measured feed, and .theta. is a vector to be estimated with "T" denoting the transpose of the vector. After inserting roughness and feed values into such vector model, taken from the last machined workpiece, the coefficients are recursively estimated by sequential regression analysis.

Dependencies are inferred from the claim text.