Improved manufacturing productivity with recursive constraint bounding

Complexity of manufacturing processes has hindered methodical specification of machine settings for improving productivity. For processes where analytical models are available (e.g., turning and grinding), modeling uncertainty caused by diversity of process conditions and time-variability has precluded the application of traditional optimization methods to minimize cost or production time. In these cases, the machine settings are selected conservatively to ensure part quality satisfaction at the expense of longer production times. In other cases, where it is prohibitively difficult to represent the process by an analytical model (e.g., injection molding), the machine settings are assigned either by trial and error, based on heuristic knowledge of an experienced operator, or statistical Design of Experiments methods which require a comprehensive empirical model between the inputs and part quality attributes. The purpose of this thesis is to present Recursive Constraint Bounding (RCB) as a general methodology for machine setting selection in manufacturing processes. In RCB, measurements of part quality attributes (e.g., size and surface integrity) are used as feedback to assess optimality/integrity of the process, and the machine settings are adjusted by formulating and solving a customized optimization problem with the objective of improving part quality or reducing production time. RCB is applied to cylindrical plunge grinding, where an approximate model is available, and injection molding, where adequate process models are unavailable. For cylindrical plunge grinding, cycle-time is minimized while satisfying constraints. For injection molding, machine settings are selected so as to satisfy part quality constraints