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Data mining for fault diagnostics : a case for plastic injection molding
ID Kozjek, Dominik (Author), ID Vrabič, Rok (Author), ID Kralj, David (Author), ID Butala, Peter (Author), ID Lavrač, Nada (Author)

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Abstract
In manufacturing processes the automated identification of faulty operating conditions that might lead to insufficient product quality and reduced availability of the equipment is an important and challenging task. This paper proposes a data mining approach to the identification of complex faults, i.e. unplanned machine stops in plastic injection molding. Several data mining methods are considered, with a focus on the abilities to reveal patterns of faulty operating conditions and on the interpretation of the induced models with the objective to find the data mining method that best corresponds to the nature of the plastic-injection-molding process and the related data. Well-known data mining methods, i.e. J48, random forests, JRip rules, naïve Bayes, and k-nearest neighbors are applied to real industrial data. The results show that tested data mining methods can be effectively used to reveal patterns related to faulty operating conditions. The interpretation capacity of the tested methods, their ability to describe the operating conditions, and to reveal patterns related to faulty operating conditions, are demonstrated and discussed.

Language:English
Keywords:fault diagnostics, plastic injection molding, data analytics, data mining, industrial data
Work type:Article (dk_c)
Typology:1.08 - Published Scientific Conference Contribution
Organization:FS - Faculty of Mechanical Engineering
Year:2019
Publication status in journal:Published
Article version:Publisher's version of article
Number of pages:f. 809-814
Numbering:Vol. 81
PID:20.500.12556/RUL-108454 This link opens in a new window
UDC:658.5.012.7:681.5(045)
ISSN on article:2212-8271
DOI:10.1016/j.procir.2019.03.204 This link opens in a new window
COBISS.SI-ID:16687643 This link opens in a new window
Publication date in RUL:03.07.2019
Views:908
Downloads:570
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Record is a part of a proceedings

Title:52nd CIRP Conference on Manufacturing Systems (CMS), Ljubljana, Slovenia, June 12-14, 2019
COBISS.SI-ID:16674843 This link opens in a new window

Record is a part of a journal

Title:Procedia CIRP
Publisher:Elsevier
ISSN:2212-8271
COBISS.SI-ID:12981019 This link opens in a new window

Secondary language

Language:Slovenian
Keywords:diagnosticiranje napak, injekcijsko brizganje plastike, analitika podatkov, podatkovno rudarjenje, industrijski podatki

Projects

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Project number:P2-0270
Name:Proizvodni sistemi, laserske tehnologije in spajanje materialov

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