Cycle-time Reduction in Machining by Recursive Constraint Bounding

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 de ne the optimal conditions, so they are still a ected by modeling inaccuracy and noise. This paper presents the method of Recursive Constraint Bounding (RCB2) which is designed to cope with modeling uncertainty as well as process time-variability. RCB2 uses a model, similar to other ACO methods. However, it considers con dence levels and noise bu ers to account for degrees of inaccuracy and randomness associated with each modeled constraint. RCB2 assesses optimality by measuring the slack in individual constraints after each part is completed (cycle), and then rede nes 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. Presently a research associate at the Nat'l Inst. of Standards and Technologies ASME Member To whom all correspondence should be addressed. ASME Fellow