Parallel optimization methods for agile manufacturing

TL;DR: The results of an LDRD is described, the goal of which was to develop optimization algorithms and software tools that will enable automated design thereby allowing for agile manufacturing.

Abstract: The rapid and optimal design of new goods is essential for meeting national objectives in advanced manufacturing. Currently almost all manufacturing procedures involve the determination of some optimal design parameters. This process is iterative in nature and because it is usually done manually it can be expensive and time consuming. This report describes the results of an LDRD, the goal of which was to develop optimization algorithms and software tools that will enable automated design thereby allowing for agile manufacturing. Although the design processes vary across industries, many of the mathematical characteristics of the problems are the same, including large-scale, noisy, and non-differentiable functions with nonlinear constraints. This report describes the development of a common set of optimization tools using object-oriented programming techniques that can be applied to these types of problems. The authors give examples of several applications that are representative of design problems including an inverse scattering problem, a vibration isolation problem, a system identification problem for the correlation of finite element models with test data and the control of a chemical vapor deposition reactor furnace. Because the function evaluations are computationally expensive, they emphasize algorithms that can be adapted to parallel computers.