Journal
CIRP ANNALS-MANUFACTURING TECHNOLOGY
Volume 63, Issue 1, Pages 449-452Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.cirp.2014.03.123
Keywords
Quality assurance; Predictive model; Complex systems
Funding
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1432914, 1437139, 1401511] Funding Source: National Science Foundation
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Early detection of changes in transient process behaviors from sensor signals is becoming essential for quality assurance in microelectronics and ultraprecision manufacturing processes. We present a Dirichlet process Gaussian State Machine (DPGSM) representation to capture complex dynamics as a random concatenation of nonlinear stationary segments, and develop a method to detect early-stage fault-inducing changes. Extensive experiments suggest that the present approach, compared to other methods tested, was able to detect slight changes that cause severe surface damage 48 ms earlier in an ultraprecision machining (UPM) process, and at least 2000 ms earlier in a chemical mechanical planarization (CMP) process. (C) 2014 CIRP.
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