Knowledge elicitation for fault diagnostics in plastic injection moulding: A case for machine-to-machine communication. Issue 1 (2017)

• Record Type: Journal Article
• Authors: Vrabič, Rok; Kozjek, Dominik; Butala, Peter
• Is Part Of: CIRP annals. Volume 66:Issue 1(2017)
• Page Start: 433
• Page End: 436
• Publication Date: 2017
• Subjects: Manufacturing system -- Predictive model -- Machine-to-machine; Production engineering -- Research -- Periodicals; 670.5
• DOI: 10.1016/j.cirp.2017.04.001
• Languages: English
• ISSNs: 0007-8506

Abstract

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.