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8 June 2022 Rapid parameter estimation of CNC feed drive systems
Quade Butler, Brett Sicard, Ethan Hughey, Youssef Ziada, David Stephenson, S. A. Gadsden
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Proceedings Volume 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI; 121221B (2022) https://doi.org/10.1117/12.2626801
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
Abstract
As Industry 4.0 evolves with the abundance of data, networking capabilities and new computing technologies, manufacturers are looking for ways to exploit this revolution. The demands of machine tools and their feed drive systems require manufacturers to optimally plan and schedule maintenance actions to minimize costs. These actions can be supplemented by capitalizing on machine data and the idea of cyber-physical systems, with the use of edge and cloud computing, by monitoring important machine characteristics. A substantial benefit to manufacturers would be the ability to monitor the health characteristics of machine tools to aid them in their maintenance planning. Some of the challenges manufacturers face with this are the computing time and effort needed to analyze and evaluate the vast amount of machine data available. A step towards real-time condition monitoring of machine characteristics includes rapid parameter estimation of CNC machine tool systems. The estimation of mass and friction allow for the monitoring of CNC feed drive health. This work proposes the estimation of such parameters from real-world industrial machine tool data. A Feed drive testing procedure is developed for smart data acquisition. Data analysis and recursive least squares methods are used to extract key parameters representative of machine health that are realizable on edge computing devices.
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Quade Butler, Brett Sicard, Ethan Hughey, Youssef Ziada, David Stephenson, and S. A. Gadsden "Rapid parameter estimation of CNC feed drive systems", Proc. SPIE 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, 121221B (8 June 2022); https://doi.org/10.1117/12.2626801
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KEYWORDS
Manufacturing
Data modeling
Modeling
Data analysis
Error analysis
Performance modeling
Servomechanisms
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