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Smart Machining Process Monitoring Enabled by Contextualized Process Profiles for Synchronization
(Received 10 September 2019; accepted 18 February 2020)
Published Online: 18 March 2020
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Repeated machine downtime leads to lost productivity, late deliveries, and dissatisfied customers. Plants often struggle with meeting delivery commitment because they lack advanced predictive technologies. Because of limited information from current production practice, it is very challenging to control product quality, meet the tight tolerance, and eliminate scrap parts, which is especially critical for expensive aeroengine components. With the recent development of Industrial Internet of Things and Operation Technologies, it is now feasible to digitize the production process and take machine reliability and performance to a new level. In this study, the machining process is simulated in the virtual environment, and the output response is used as a reference digital thread of the process. In real-time machining, the computer numerical control (CNC) control signals together with existing machinery sensors and historical data are monitored and fused together to observe the machining conditions. Comparing the reference signature with supervised machine learning analytics, any subtle deviations in operating behavior, which are often the early warning signs of problems, can be identified. Also, with the look-ahead function, it gives the operators better visibility into the machining process. With the proposed method, without adding any more hardware to existing production machineries, it is feasible to capture data in real time, conduct automated analysis of the information, and create visualizations for team members. Plus, the real-time monitoring of any deviation in operating behavior during production can protect machine and cutting tools from excessive load and damage, thereby reducing operating costs and increasing effective manufacturing capacity.
Physics Science Department, United Technologies Research Center, East Hartford, CT
Wagner, Timothy C.
Emerging Technologies Program Office, United Technologies Research Center, East Hartford, CT
Digital Thread Center of Excellence, United Technologies Corporation, East Hartford, CT
Stock #: SSMS20190040
Author Zhigang Wang, Timothy C. Wagner, Changsheng Guo
Title Smart Machining Process Monitoring Enabled by Contextualized Process Profiles for Synchronization
Symposium , 0000-00-00