NDLI: A new approach for manufacturing forecast problems with insufficient data: the case of TFT

Content Provider	Springer Nature Link
Author	Li, Der Chiang Chang, Chih Chieh Liu, Chiao Wen Chen, Wen Chih
Copyright Year	2011
Abstract	Manufacturing forecast problems have been widely discussed in recent years, where more accurate predictions could reduce the overall manufacturing costs. This study uses the case of ensuring the heights of thin film transistor–liquid crystal display photo-spacers. It is a small sample size prediction problem, because the data available for analysis is limited on the manufacturing lines. A new approach is developed to deal with this problem, which involves three steps. The first step is using K-means clustering to separate data into K clusters, while the second step is to compute the possibility through the fuzzy membership function in each cluster for attribute extension. The last step is to put the data with new generate attributes into a backpropagation neural network (BPNN) machine learning algorithm. Two performance evaluation methods, cross-validation and data specification testing, are selected to compare the proposed method with three popular prediction models: linear regression, support vector machine for regression (SVR), and BPNN. The results show that the proposed method outperforms the others with regard to the total errors, mean square error, and standard deviation.
Starting Page	225
Ending Page	233
Page Count	9
File Format	PDF
ISSN	09565515
Journal	Journal of Intelligent Manufacturing
Volume Number	24
Issue Number	2
e-ISSN	15728145
Language	English
Publisher	Springer US
Publisher Date	2011-07-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Forecasting K-means Small data set TFT–LCDs BPNN Production/Logistics/Supply Chain Manufacturing, Machines, Tools Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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