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

Published

Author(s)

Robert W. Ivester, Jarred C. Heigel

Abstract

A critical aspect of smart machining systems is the appropriate management of knowledge and information to support effective decision-making. Uncertainty associated with 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 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 under conditions allowing for modeling and measurement uncertainties.
Citation
Transactions of the North American Research Institute (NAMRI)/SME
Volume
Vol. 35

Keywords

Adaptive Control Optimization, Modeling Uncertainty, Robust Optimization, Smart Machining Systems

Citation

Ivester, R. and Heigel, J. (2007), Smart Machining Systems: Robust Optimization and Adaptive Control Optimization for Turning Operations, Transactions of the North American Research Institute (NAMRI)/SME, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822723 (Accessed May 22, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created May 22, 2007, Updated February 19, 2017