Data Mining Definitions and Applications for the Management of Production Complexity

被引：22

作者：

Schuh, Guenther ^{[1
]}

Reinhart, Gunther ^{[2
]}

Prote, Jan-Philipp ^{[1
]}

Sauermann, Frederick ^{[1
]}

Horsthofer, Julia ^{[2
]}

Oppolzer, Florian ^{[1
]}

Knoll, Dino ^{[2
]}

机构：

[1] Rhein Westfal TH Aachen, Lab Machine Tools & Prod Engn WZL, D-52074 Aachen, Germany

[2] Tech Univ Munich, Inst Machine Tools & Ind Management Iwb, D-85748 Garching, Germany

来源：

52ND CIRP CONFERENCE ON MANUFACTURING SYSTEMS (CMS) | 2019年 / 81卷

关键词：

data mining; machine learning; artificial intelligence; production complexity;

D O I：

10.1016/j.procir.2019.03.217

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Production complexity has increased considerably in recent years due to increasing customer requirements for individual products. At the same time, continuous digitization has led to the recording of extensive, granular production data. Research claims that using production data in data mining methods can lead to managing production complexity effectively. However, manufacturing companies widely do not use such data mining methods. In order to support manufacturing companies in utilizing data mining, this paper presents both a literature review on definitions of data mining, artificial intelligence and machine learning as well as a categorization of existing approaches of applying data mining to manage production complexity. (C) 2019 The Authors. Published by Elsevier Ltd.

引用

页码：874 / 879

页数：6

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