Smart Machining Systems: Robust Optimization and Adaptive Control Optimization for Turning Operations

A critical aspect of smart machining systems is the appropriate management of knowledge and information to support effective decision-making. The uncertainty of model-based predictions of machining performance plays an important role in decision-making for machining optimization and adaptive control optimization. This paper presents a technique for managing modeling and measurement uncertainties for optimization and control. The resulting model provides a basis for predicting cutting performance to facilitate effective decision-making in a real-time control environment. The cutting performance is optimized when a balance of quality improvement versus cost reduction is obtained. The approach is demonstrated for an American Iron and Steel Institute (AISI) 1045 steel workpiece machined on a lathe under a range of controlled process conditions. Measurements of product quality resulting from the changes in process conditions form a basis for model-based robust optimization and adaptive control optimization that address uncertainties encountered in production environments.