Intelligent maintenance prediction system for LED wafer testing machine

Chien-Chang Hsu () and Min-Sheng Chen
Additional contact information
Chien-Chang Hsu: Fu-Jen Catholic University
Min-Sheng Chen: Fu-Jen Catholic University

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 2, No 5, 335-342

Abstract: Abstract Achieving high quality production of light-emitting diode (LED) wafers requires robust monitoring and the use of a stable test machine. In many factories, production continues 24 h a day. Stopping the manufacturing process at a factory is often difficult. Therefore, reducing inspection time and ensuring the stability of test machines are important. Traditionally, LED wafer factories examine their test machines during periodic maintenance. Standard lamp adjustments are performed to ensure their accuracy. This process interrupts the manufacturing process and requires extra manpower. It reduces productivity and increases production cost. Additionally, the accurate assessment of the aging of the components of the machine requires an experienced engineer. Correctly timing the maintenance and replacing the aging components of the LED wafer test machine are important. This work performed feature extraction to identify the working attributes of an LED wafer test machine. The intelligent maintenance prediction system then uses the radial basis function neural network and variability of the working attributes to predict the maintenance times and aging of the LED wafer test machines. Experimental results reveal that the accuracy of proposed system in predicting maintenance times exceeds 98 %.

Keywords: LED wafer test machine; Intelligent maintenance prediction; Feature extraction; RBF neural network; Working attributes variability; Aging or failure (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-013-0866-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:27:y:2016:i:2:d:10.1007_s10845-013-0866-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-013-0866-3

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().