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Knowledge elicitation for fault diagnostics in plastic injection moulding : a case for machine-to-machine communication
Vrabič, Rok (Author), Kozjek, Dominik (Author), Butala, Peter (Author)

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Abstract
In most manufacturing processes the defect rate is very low. Sometimes, only a few parts per million are defective because of a faulty process. For this reason, fault diagnostics is faced with extremely imbalanced data sets and requires large volumes of data to achieve a reasonable performance. This paper explores whether a machine-to-machine approach can be used, in which several work systems share the process data to improve the accuracy of the fault-detection model. The model is based on machine learning and is applied to industrial data from approximately two million process cycles performed on several injection moulding work systems.

Language:English
Keywords:manufacturing system, predictive model, machine-to-machine
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Year:2017
Number of pages:Str. 433-436
Numbering:Vol. 66, iss. 1
UDC:658.5(045)
ISSN on article:0007-8506
DOI:10.1016/j.cirp.2017.04.001 This link opens in a new window
COBISS.SI-ID:15490587 This link opens in a new window
Views:551
Downloads:140
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Record is a part of a journal

Title:CIRP annals
Shortened title:CIRP ann.
Publisher:Elsevier
ISSN:0007-8506
COBISS.SI-ID:170267 This link opens in a new window

Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:P2-0270
Name:

Secondary language

Language:Slovenian
Keywords:proizvodni sistemi, napovedni modeli, komunikacija stroj-stroj

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