Knowledge elicitation for fault diagnostics in plastic injection moulding : a case for machine-to-machine communication

Vrabič, Rok (avtor), Kozjek, Dominik (avtor), Butala, Peter (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: manufacturing system, predictive model, machine-to-machine
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Različica publikacije: Recenzirani rokopis
• Leto izida: 2017
• Št. strani: Str. 433-436
• Številčenje: Vol. 66, iss. 1
• PID: 20.500.12556/RUL-101580
• UDK: 658.5(045)
• ISSN pri članku: 0007-8506
• DOI: 10.1016/j.cirp.2017.04.001
• COBISS.SI-ID: 15490587
• Datum objave v RUL: 18.06.2018

Gradivo je del revije

• Naslov: CIRP annals
• Skrajšan naslov: CIRP ann.
• Založnik: Technische Rundschau, Hallwag Verlag, Colibri, Elsevier
• ISSN: 0007-8506
• COBISS.SI-ID: 170267

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: proizvodni sistemi, napovedni modeli, komunikacija stroj-stroj

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0270
• Naslov: Proizvodni sistemi, laserske tehnologije in spajanje materialov