The dynamic transfer batch-size decision for thin film transistor-liquid crystal display array manufacturing by artificial neural-network

Ta-Ho Yang, Yu An Shen

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In the thin film transistor-liquid crystal display (TFT-LCD) manufacturing process, array manufacturing is an important process. Transporting activities in array manufacturing are an important factor because of the frequent tasks. The transporting activity in array manufacturing is performed by an automated material handling system (AMHS). Automated guided vehicle (AGV) is the transporter used to carry glass substrates that are stored in a cassette. The capacity of a cassette is known as the transfer batch-size. Prior research of decisions in transfer batch-size, has addressed an optimal methodology, where one optimal transfer batch-size is assumed to have known conditions. However, in the volatile production environment, there may be multiple kinds of transfer batch-sizes. Therefore, we present an application of using a dynamic transfer batch-size strategy within a volatile production environment. In order to obtain the appropriate transfer batch-size for the current production environment, we adopt a neural-network based methodology as the core of the decision-making mechanism. This mechanism has the capability to identify the suitable transfer batch-size to allow an effective and efficient transportation under numerous conditions within the current production environment. This methodology is compared with the fixed transfer batch-size strategy in a real practical case. The results show that the dynamic transfer batch-size is superior to the fixed batch-size transportation.

Original languageEnglish
Pages (from-to)769-776
Number of pages8
JournalComputers and Industrial Engineering
Volume60
Issue number4
DOIs
Publication statusPublished - 2011 May 1

Fingerprint

Thin film transistors
Liquid crystal displays
Neural networks
Materials handling
Decision making
Glass
Substrates

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

Cite this

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title = "The dynamic transfer batch-size decision for thin film transistor-liquid crystal display array manufacturing by artificial neural-network",
abstract = "In the thin film transistor-liquid crystal display (TFT-LCD) manufacturing process, array manufacturing is an important process. Transporting activities in array manufacturing are an important factor because of the frequent tasks. The transporting activity in array manufacturing is performed by an automated material handling system (AMHS). Automated guided vehicle (AGV) is the transporter used to carry glass substrates that are stored in a cassette. The capacity of a cassette is known as the transfer batch-size. Prior research of decisions in transfer batch-size, has addressed an optimal methodology, where one optimal transfer batch-size is assumed to have known conditions. However, in the volatile production environment, there may be multiple kinds of transfer batch-sizes. Therefore, we present an application of using a dynamic transfer batch-size strategy within a volatile production environment. In order to obtain the appropriate transfer batch-size for the current production environment, we adopt a neural-network based methodology as the core of the decision-making mechanism. This mechanism has the capability to identify the suitable transfer batch-size to allow an effective and efficient transportation under numerous conditions within the current production environment. This methodology is compared with the fixed transfer batch-size strategy in a real practical case. The results show that the dynamic transfer batch-size is superior to the fixed batch-size transportation.",
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