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

.pdfPDF - Presentation file, Download (449,99 KB)
MD5: 0F455D7F6360FE485A5EC13CDFD3924A

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.

Keywords:manufacturing system, predictive model, machine-to-machine
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Publication version:Author Accepted Manuscript
Number of pages:Str. 433-436
Numbering:Vol. 66, iss. 1
PID:20.500.12556/RUL-101580 This link opens in a new window
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
Publication date in RUL:18.06.2018
Copy citation
Share:Bookmark and Share

Record is a part of a journal

Title:CIRP annals
Shortened title:CIRP ann.
Publisher:Technische Rundschau, Hallwag Verlag, Colibri, Elsevier
COBISS.SI-ID:170267 This link opens in a new window

Secondary language

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


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

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections: