Using data mining methods to develop manufacturing production rule in IoT environment

In order to meet the needs of customers in an Internet of Things (IoT) environment, the traditional manufacturing production strategy has gradually shifted from mass production to a small number of diverse forms. In traditional industry, when the production type changes to a small number of diverse...

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Vydáno v:The Journal of supercomputing Ročník 78; číslo 3; s. 4526 - 4549
Hlavní autoři: Wang, Lei, Lin, Bin, Chen, Rongli, Lu, Kuan-Han
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.02.2022
Springer Nature B.V
Témata:
Assembly lines
Compilers
Computer Science
Data mining
Data Mining for IoT in Mobile Edge computing
Delivery scheduling
Internet of Things
Interpreters
Manufacturing
Mass production
Processor Architectures
Production lines
Production management
Production methods
Production scheduling
Programming Languages
Schedules
Car manufacturing production
Data mining
Association rules
Internet of things (IoT)
ISSN:0920-8542, 1573-0484
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Shrnutí:In order to meet the needs of customers in an Internet of Things (IoT) environment, the traditional manufacturing production strategy has gradually shifted from mass production to a small number of diverse forms. In traditional industry, when the production type changes to a small number of diverse forms, the complexity of scheduling increases and the rules of adaptability between products and production lines is not easy to judge. However, in traditional production management scheduling, the adaptability of production lines is mostly planned based on past experience. If the number of orders is too large or the production schedule changes, errors will increase. This situation will cause the actual production situation to be far removed from the planned result, which will affect the schedule achievement and delivery time. The present paper reports research using association rules to explore production lines and apply logic to solve the problem of production rules between production lines and products in the car manufacturing industry. The results show that the application of data mining association rules has an accuracy above 87%. The application of data mining can provide manufacturing production rules to assist managers to make better decisions in the IoT environment and to reduce the time required for manufacturing production.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-021-04034-6