A learning and estimation problem arising from in situ control and diagnostics of manufacturing processes

We analyze the problem of estimating product variables from process measurements in manufacturing systems. In particular, a novel approach for studying the performance of such estimators in terms of their expected performance is introduced. Using ideas from statistical learning theory, we obtain sufficient conditions on the manufacturing process, the estimation algorithm, and the design procedure to guarantee asymptotic convergence of the estimation algorithm to some optimal estimator when the available data goes to infinity