A framework of integrated product design and control in manufacturing polymer nanocomposites
Date
2014
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Publisher
University of Delaware
Abstract
Polymer nanocomposites are increasingly used across a variety of applications in the plastics sectors, especially in the packaging and automotive industries. The successful development and commercialization of polymer nanocomposites requires an appropriate product design, followed by an efficient process for manufacturing the products to achieve desired end-use product performance consistently. While product design of nanocomposites has been receiving deserved attention, the equally important next step--a control scheme designed for ensuring that what the manufacturing process produces will perform as designed--receives little or even no attention. Since it is the customer that evaluates the product performance, the control strategy required for achieving the objectives of product design must extend well beyond the traditional control scheme of merely controlling process output variables and even beyond the end-use characteristics control; it must explicitly incorporate customer feedback on the product performance, in order to ascertain consistent attainment of desired product end-use performance. However, this feedback is completely missing from the control schemes. To address these challenging issues, we proposed a framework for integrating product design with appropriate control strategies required for achieving acceptable product performance consistently. This framework is illustrated by manufacturing polymer nanocomposites using extrusion processes. The major contributions of this dissertation include: (i) product design: the manufacturing material, consequent manufacturing process, and required operating conditions in manufacturing polymer nanocomposites were determined judiciously, (ii) quantification of clay dispersion in polymer nanocomposites: this novel method based on describing particle length distribution from transmission electron microscopy micrographs with a gamma probability model; an explicit quantitative relationship between these model parameters and dispersion level was established successfully, and (iii) control scheme design and implementation: a multivariable cascade-type control scheme is designed, consisting of controller C1 for controlling process outputs, controller C2 for regulating the product end-use properties, and controller C3 for improving customer satisfaction by incorporating customer feedback data into the overall control scheme. C1 and C2 were designed as model predictive controllers, while C3 was designed as an unconventional customer feedback controller utilizing customer feedback data to take rational corrective action if the product does not perform in end-use precisely as designed.