Feedrate Optimization via Pass-to-Pass Learning–Applied to 2.5D Contour Machining under Servo Error and Spindle Torque Constraints

Repeated machining passes (i.e., continuous toolpaths) are common in CNC manufacturing, including multi-level machining of prismatic parts and iso-contour passes in contour machining. They present an opportunity to exploit pass-to-pass learning to improve productivity without sacrificing quality through feedrate optimization. Traditional iterative learning methods provide a means to exploit pass-to-pass learning for quality improvements, but they are not well-suited to feedrate optimization because the reference trajectories change as the feedrate increases. In the authors’ prior work, learning-based feedrate optimization was demonstrated for repeated machining along identical toolpaths. This paper extends that concept to the more challenging case of similar but non-identical cutting paths, as encountered in contour machining. A pass-to-pass learning strategy is proposed in which corresponding sections of non-identical iso-contour passes are identified using a contour-matching method based on geometric similarity. Bayesian linear regression models are then used to learn and predict contour error and spindle torque across passes, with uncertainty explicitly quantified through credible intervals. These predictions are embedded in a window-based feedrate optimization framework solved via sequential linear programming, enabling feedrate maximization subject to kinematic, contour-error, and spindle-torque constraints. The proposed approach is experimentally validated on a 3-axis desktop CNC milling machine through multiple 2.5D contour machining case studies. Results show that the method can rapidly approach near-optimal feedrates after only a few passes, culminating in up to 16.4% increase in productivity compared to an equivalent learning-based feedrate optimization approach for identical toolpaths.