This grant provides funding to develop and analyze statistically-based experimental designs especially suited for multiple stage manufacturing, and to integrate these designs with reliability modeling for new product development. The concept of a multiple stage fractional factorial split plot (MSFFSP) design is proposed to accommodate complex manufacturing processes where restrictions on the randomization of experimental runs are required. Specifically, a general linear model, design catalogs, and variance estimates of the MSFFSP design will be derived. This work will be applied to a multiple stage process involving gel-casting lithography to manufacture a multifunctional forceps-scissors (FS) instrument for minimally invasive surgery. The novel lithography process uses nanoparticulates to impart the FS instrument with both nano- and micro- scale features. The MSFFSP design will also be applied in the measurement system analysis of an optical microscope, which is used to collect data on the FS device. In conjunction with the FS device development, failure modes will be identified, enumerated, and transformed into reliability data. The concept of accelerated testing will be used to shorten the time to acquire this data and will be integrated with MSFFSP design. The MSFFSP analysis will be modified in order to properly analyze the reliability data, which has different statistical characteristics than process and product data. If successful, the results of this research will lead to a reduction in the number of trial runs and settings required for experimentation in complex manufacturing processes with multiple stages while maintaining design efficiency. Using these designs and integrating them with reliability testing into modeling and production for micro- and nano-scale manufacturing research will yield strategic advantages by speeding the research and development cycle, stretching the experimental budget, and ultimately helping to create more reliable, robust, and better performing small-scale products. The proposed work will not only advance the field of experimental design, but also amplify the potential of quality methodologies to be extended for other multiple stage manufacturing processes.
