Process Monitoring Using Online Sensor Signals

2017 ◽  
pp. 129-144
Keyword(s):  
Process Monitoring ◽  
Online Sensor ◽  
Sensor Signals

Real-Time Identification of Incipient Surface Morphology Variations in Ultraprecision Machining Process

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Prahalad Rao ◽  
Satish Bukkapatnam ◽  
Omer Beyca ◽  
Zhenyu (James) Kong ◽  
Ranga Komanduri
Keyword(s):  
Surface Morphology ◽  
Real Time ◽  
Process Monitoring ◽  
Diamond Turning ◽  
Machining Process ◽  
Complex Signal ◽  
Ultraprecision Machining ◽  
Real Time Monitoring ◽  
Process Dynamics ◽  
Sensor Signals

Real-time monitoring and control of surface morphology variations in their incipient stages are vital for assuring nanometric range finish in the ultraprecision machining (UPM) process. A real-time monitoring approach, based on predicting and updating the process states from sensor signals (using advanced neural networks (NNs) and Bayesian analysis) is reported for detecting the incipient surface variations in UPM. An ultraprecision diamond turning machine is instrumented with three miniature accelerometers, a three-axis piezoelectric dynamometer, and an acoustic emission (AE) sensor for process monitoring. The machine tool is used for face-turning aluminum 6061 discs to a surface finish (Ra) in the range of 15–25 nm. While the sensor signals (especially the vibration signal in the feed direction) are sensitive to surface variations, the extraneous noise from the environment, machine elements, and sensing system prevents direct use of raw signal patterns for early detection of surface variations. Also, nonlinear and time-varying nature of the process dynamics does not lend conventional statistical process monitoring techniques suitable for characterizing UPM-machined surfaces. Consequently, instead of just monitoring the raw sensor signal patterns, the nonlinear process dynamics wherefrom the signal evolves are more effectively captured using a recurrent predictor neural network (RPNN). The parameters of the RPNN (weights and biases) serve as the surrogates of the process states, which are updated in real-time, based on measured sensor signals using a Bayesian particle filter (PF) technique. We show that the PF-updated RPNN can effectively capture the complex signal evolution patterns. We use a mean-shift statistic, estimated from the PF-estimated surrogate states, to detect surface variation-induced changes in the process dynamics. Experimental investigations show that variations in surface characteristics can be detected within 15 ms of their inception using the present approach, as opposed to 30 ms or higher with the conventional statistical change detection methods tested.

Functional versus schematic overview displays: Impact on operator situation awareness in process monitoring

2010 ◽  
Author(s):  
Anand Tharanathan ◽  
Jason Laberge ◽  
Peter Bullemer ◽  
Dal Vernon Reising ◽  
Rich McLain
Keyword(s):  
Process Monitoring ◽  
Situation Awareness ◽  
Functional Versus

User-friendly statistical concepts for process monitoring

1998 ◽  
Vol 49 (9) ◽  
pp. 976-985
Author(s):  
M Wood ◽  
N Capon ◽  
and M Kaye
Keyword(s):  
Process Monitoring ◽  
User Friendly

Measurement Sites for Wafer Map Visualization and Process Monitoring

2010 ◽  
Vol 130 (11) ◽  
pp. 1987-1993
Author(s):  
Makoto Ono ◽  
Hirohito Hayashi ◽  
Akira Kondo ◽  
Daisuke Hamaguchi ◽  
Shun'ichi Kaneko
Keyword(s):  
Process Monitoring ◽  
Map Visualization
Sign in / Sign up

Export Citation Format

Share Document