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Open AccessArticle

Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm

Department of Mechatronics Engineering, National Changhua University of Education, No. 2, Shida Rd., Changhua city, Changhua 500, Taiwan
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Appl. Sci. 2019, 9(20), 4241; https://doi.org/10.3390/app9204241 (registering DOI)
Received: 22 August 2019 / Revised: 6 October 2019 / Accepted: 8 October 2019 / Published: 11 October 2019
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
The prognosis and management of machine health statuses are emerging research topics. In this study, the performance degradation of a wafer-handling robot arm (WHRA) was predicted using the proposed machine-learning approach. This method considers the eccentric vertical and planar position deviations from a wafer mark using a charge-coupled device (CCD) camera. Synthesized position signals were defined using the square root of x- and y-axes deviations in the horizontal view and the square of the wafer mark diameter in the vertical view. A feature extraction method was used to determine the position status on the basis of these displacements and the area of a wafer mark in a CCD image. The root mean square error and mean, maximum, and minimum of the synthesized position signals were extracted through feature extraction and used for data mining by a general regression neural network (GRNN) and logistic regression (LR) models. The lifetime assessment by confidence value of the WHRA’s remaining useful life (RUL) by the genetic algorithm/GRNN exhibited nearly the same trend as that predicted through a run-to-failure LR model. The experimental results indicated that the proposed methodology can be used for proactive assessments of the RUL of WHRAs. View Full-Text
Keywords: prognostic and health management; wafer-handling robot arm; feature extraction; general regression neural network; logistic regression prognostic and health management; wafer-handling robot arm; feature extraction; general regression neural network; logistic regression
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Huang, Y.-C.; Yang, Z.-S.; Liao, H.-S. Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm. Appl. Sci. 2019, 9, 4241.

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