Stochastic methods for improving secondary production decisions under compositional uncertainty by

A key element for realizing long term sustainable use of any metal will be a robust secondary recovery industry. Secondary recovery forestalls depletion of non-renewable resources and avoids the deleterious effects of extraction and winning (albeit by substituting some effects of its own). For most metals, the latter provides strong motivation for recycling; for light metals, like aluminum, the motivation is compelling. Along aluminum’s life-cycle there are a variety of leverage points for increasing the usage of secondary or recycled materials. This thesis aims to improve materials decision making in two of these key areas: 1) blending decisions in manufacturing, and 2) alloy design decisions in product development. The usage of recycled aluminum in alloy blends is greatly hindered by variation in the raw material composition. Currently, to accommodate compositional variation, firms commonly set production targets well inside the window of compositional specification required for performance reasons. Window narrowing, while effective, does not make use of statistical sampling data, leading to sub-optimal usage of recycled materials. This work explores the use of stochastic programming techniques which allow explicit consideration of statistical information on composition. The computational complexity of several methods is quantified in order to select a single method for comparison to deterministic models, in this case, a chance-constrained model was optimal. The framework and a case study of cast and wrought production with available scrap materials are presented. Results show that it is possible to increase the use of recycled material without compromising the likelihood of batch errors, when using this method compared to conventional window narrowing. The chance-constrained framework was then extended to improving the alloy design process. Currently, few systematic methods exist to measure and direct the metallurgical alloy design process to create alloys that are most able to be produced from scrap. This is due, in part, to the difficulty in evaluating such a context-dependent property as recyclability of an alloy, which will depend on the types of scraps available to producers, the compositional characteristics of those scraps, their yield, and the alloy itself. Results show that this method is effective in, a) characterizing the challenge of developing recycling-friendly alloys due to the contextual sensitivity of that property; b) demonstrating how such models can be used to evaluate the potential scrap usage of alloys; and (c) exploring the value of sensitivity analysis information to proactively identify effective alloy modifications that can drive increased potential scrap use. Thesis Supervisor: Olivier L. de Weck Title: Associate Professor of Aeronautics and Astronautics & Engineering Systems