Methods of Data Mining for Quality Assurance in Glassworks

被引：1

作者：

Pasko, Lukasz ^{[1
]}

Litwin, Pawel ^{[1
]}

机构：

[1] Rzeszow Univ Technol, Fac Mech Engn & Aeronaut, Al Powstancow Warszawy 12, PL-35959 Rzeszow, Poland

来源：

COLLABORATIVE NETWORKS AND DIGITAL TRANSFORMATION | 2019年

关键词：

Big Data; Data mining; Quality assurance; Glass industry; MECHANICAL-PROPERTIES; PROCESS PARAMETERS;

D O I：

10.1007/978-3-030-28464-0_17

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In manufacturing enterprises implementing the idea of Industry 4.0, devices that generate data are increasingly used. Over time, huge data sets are created. These collections, known as Big Data, are very important to the company because they can contain valuable information. One of the goals of today's enterprises is to discover this information and transform it into knowledge. The aim of the article is to present the methodology of exploration of large data sets from the manufacturing process in glassworks. The result of the research is knowledge about the parameters of the manufacturing process causing defects in the products.

引用

页码：185 / 192

页数：8

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