Development of a new machine learning-based informatics system for product health monitoring

Moschos Papananias*, Olusayo Obajemu, Thomas E. McLeay, Mahdi Mahfouf, Visakan Kadirkamanathan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Manufacturing informatics aims to optimize productivity by extracting information from numerous data sources and making decisions based on that information about the process and the parts being produced. Manufacturing processes usually include a series of costly operations such as heat treatment, machining, and inspection to produce high-quality parts. However, performing costly operations when the product conformance to specifications cannot be achievable is not desirable. This paper develops a new machine learning-based informatics system capable of predicting the end product quality so that non-value-adding operations such as inspection can be minimized and the process can be stopped before completion when the part being manufactured fails to meet the design specifications.

Original languageEnglish
Title of host publicationProceedings of the 53rd CIRP Conference on Manufacturing Systems
PublisherElsevier
Pages473-478
Number of pages6
DOIs
Publication statusPublished - 22 Sep 2020
Event53rd CIRP Conference on Manufacturing Systems - Chicago, United States
Duration: 1 Jul 20203 Jul 2020

Publication series

NameProcedia CIRP
PublisherElsevier
Volume93
ISSN (Print)2212-8271

Conference

Conference53rd CIRP Conference on Manufacturing Systems
Abbreviated titleCMS 2020
Country/TerritoryUnited States
CityChicago
Period1/07/203/07/20

Keywords

  • artificial neural networks
  • manufacturing informatics
  • multiple linear regression
  • multistage manufacturing process
  • principal componet analysis
  • UKRI
  • EPSRC
  • EP/P006930/1

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