Optimization of Cylindrical Plunge Grinding by Recursive Constraint Bounding

Modeling uncertainty in machining, caused by modeling bias and 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 the use of 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 bias and noise. The present paper discusses the method of Recursive Constraint Bounding (RCB2), which is designed to cope with model uncertainty as well as process time-variability. RCB2 uses a model, similar to other ACO methods. However, it considers confidence levels to account for the degree of accuracy associated with each modeled constraint. RCB2 assesses optimality by measuring the slack in individual constraints after each part is completed (cycle), and subsequently re-defines the constraints to yield more aggressive machine settings for the next cycle. The application of RCB2 is demonstrated here in reducing cycle-time for internal cylindrical plunge grinding.