Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data

Optical dimensional metrology (ODM) technology that produces spatially dense surface measurement data is increasingly employed for quality-control purposes in discrete parts manufacturing. Such data contain a wealth of information on the surface dimensional characteristics of individual parts and on the nature of part-to-part variation. The large body of prior quality-control work on analyzing dimensional metrology data has focused heavily on fitting parametric geometric features such as circles or planes to the data for individual parts and checking whether the features are within specifications; and subsequent analysis of part-to-part variation is restricted to those specific features. In this article, we present an approach for identifying and visualizing the nature of part-to-part variation in a more general manner that is not restricted to a prespecified set of parametric features. The basis for the approach is manifold learning applied to the collective ODM data for a set of measured parts. Particular emphasis is on handling the extremely high dimensionality of ODM data.