We have developed models for optimal sensor placement for manufacturing systems with single fixture/station [9] and for multi-station manufacturing processes (MMP) with end-of-line sensing [7] or with distributed in-process sensing [11, 23]

(ii) Developing methodology for analysis of product feature-based measurement error in coordinate 

Coordinate measurement systems (CMSs) dominate dimensional control and diagnostics of various manufacturing processes.  However, CMSs have inherent errors caused by the lack of tracing ability for some of the measured part features, which are important for product inspection and process variation reduction in number of automated manufacturing systems, such as for example automotive body assembly process.  The lack of tracing ability of feature is such that instead of measuring the given feature, the CMS may actually measure the area around the selected feature.  In our research, a principle of part feature tracing ability and resultant feature-based measurement error analysis are investigated to estimate the aforementioned deficiencies in the CMSs.  Our research explores the impact of feature type and part(s) positional variation on the feature-based measurement error [26]. The proposed approach is applicable for both contact and non-contact CMSs including coordinate measuring machines (CMMs) and optical coordinate measuring machines (OCMMs).  Our research shows that the developed feature-based measurement error can have significant impact on the accuracy of measurements and on process control and diagnostics algorithms currently used in manufacturing.