An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes from heterogeneous sensor data

The objective of this work is to realize real-time monitoring of process conditions in advanced manufacturing using multiple heterogeneous sensor signals. To achieve this objective we propose an approach invoking the concept of sparse estimation called online sparse estimation-based classification (...

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Bibliographic Details
Published in:IIE transactions 2016-07, Vol.48 (7), p.579-598
Main Authors: Bastani, Kaveh, Rao, Prahalad K., Kong, Zhenyu (James)
Format: Article
Language:English
Subjects:
additive manufacturing
Artificial intelligence
Bayesian analysis
Classification
compressive sensing
Discriminant analysis
Estimating techniques
greedy Bayesian estimation
heterogeneous sensor data
Linear systems
Manufacturing
Mathematical analysis
Monitoring
Online
Real time
real-time process monitoring
semiconductor chemical-mechanical planarization
Semiconductors
Sensors
Sparse estimation
Studies
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Summary:The objective of this work is to realize real-time monitoring of process conditions in advanced manufacturing using multiple heterogeneous sensor signals. To achieve this objective we propose an approach invoking the concept of sparse estimation called online sparse estimation-based classification (OSEC). The novelty of the OSEC approach is in representing data from sensor signals as an underdetermined linear system of equations and subsequently solving the underdetermined linear system using a newly developed greedy Bayesian estimation method. We apply the OSEC approach to two advanced manufacturing scenarios, namely, a fused filament fabrication additive manufacturing process and an ultraprecision semiconductor chemical-mechanical planarization process. Using the proposed OSEC approach, process drifts are detected and classified with higher accuracy compared with popular machine learning techniques. Process drifts were detected and classified with a fidelity approaching 90% (F-score) using OSEC. In comparison, conventional signal analysis techniques-e.g., neural networks, support vector machines, quadratic discriminant analysis, naïve Bayes-were evaluated with F-score in the range of 40% to 70%.
ISSN:0740-817X
2472-5854
1545-8830
2472-5862
DOI:10.1080/0740817X.2015.1122254