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Methodical specification of process inputs for injection molding is hindered by the absence of accurate analytical models. For these processes, the input variables are assigned either by trial and error, based on heuristic knowledge of an experienced operator, or by statistical Design of Experiments (DOE) methods which construct a comprehensive empirical model between the inputs and part quality attributes. In this paper, an iterative method of input selection (tuning) referred to as the Virtual Search Method (VSM) is introduced that conducts most of the search for appropriate machine inputs in a 'virtual' environment provided by an approximate input-output (I-0) model. VSM applies the inputs to the process only when it has exhausted the search based on the current I-O model. It evaluates the quality of inputs from the search and updates the I-O model for the next round of search based on measurements of part quality attributes (e.g., size tolerances and surface integrity) after each process iteration. According to this strategy, VSM updates the model only when needed, and thus selectively develops the model as required for tuning the process. This approach has been shown to lead to shorter tuning sessions than required by DOE methods.

Modeling uncertainty in machining, caused by modeling inaccuracy, noise and process time-variability due to tool wear, hinders application of traditional optimization to minimize cost or production time. Process time-variability can be overcome by adaptive control optimization (ACO) to improve machine settings in reference to process feedback so as to satisfy constraints associated with part quality and machine capability. However, ACO systems rely on process models to define the optimal conditions, so they are still affected by modeling inaccuracy and noise. This paper presents the method of Recursive Constraint Bounding (RC[B.sub.2]) which is designed to cope with modeling uncertainty as well as process time-variability. RC[B.sub.2] uses a model, similar to other ACO methods. However, it considers confidence levels and noise buffers to account for degrees of inaccuracy and randomness associated with each modeled constraint. RC[B.sub.2] assesses optimality by measuring the slack in individual constraints after each part is completed (cycle), and then redefines the constraints to yield more aggressive machine settings for the next cycle. The application of RC[B.sub.2] is demonstrated here in reducing cycle-time for internal cylindrical plunge grinding.