Process Monitoring of Turning and Model Adaptation for Smart Machining Systems
Jarred C. Heigel, Robert W. Ivester
Smart Machining Systems improve manufacturing efficiency using optimization based on process models. Cutting force models developed from a narrow set of empirical data provide insight into the physical properties of cutting, but the extreme physical phenomena of metal cutting and practical limits on control of environment and material homogeneity, hinder predictability. In order to improve the practicality of model based decision making in an industrial machining environment, this paper introduces a method to adapt parameters of a traditional empirical model in response to on-line measures of process performance to cope with these practical limitations on process predictability. This method enables Smart Machines to self monitor production performance and adapts models and process parameters in response to uncontrollable disturbances, reducing the need for expensive empirical tests.
and Ivester, R.
Process Monitoring of Turning and Model Adaptation for Smart Machining Systems, Journal of Manufacturing Systems, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824635
(Accessed June 6, 2023)