Abstract
This paper presents a novel method that utilizes in-process measurements of product quality and models that relate those measurements with the underlying manufacturing process parameters to drive down the product quality errors via strategic adjustments of the controllable process parameters. Uniqueness of the new method is its robustness to inevitable inaccuracies in the underlying models, as well as the absence of traditional, but restrictive assumptions of Gaussianity and independence of measurement and process noise terms. The new approach was demonstrated using models and data from an automotive cylinder head machining process and an industrial-scale semiconductor lithography overlay process.
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