An intelligent metrology informatics system based on neural networks for multistage manufacturing processes

Moschos Papananias*, 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

The ability to gather manufacturing data from various workstations has been explored for several decades and the advances in sensory and data acquisition techniques have led to the increasing availability of high-dimensional data. This paper presents an intelligent metrology informatics system to extract useful information from Multistage Manufacturing Process (MMP) data and predict part quality characteristics such as true position and circularity using neural networks. The input data include the tempering temperature, material conditions, force and vibration while the output data include comparative coordinate measurements. The effectiveness of the proposed method is demonstrated using experimental data from a MMP.

Original languageEnglish
Title of host publicationProceedings of the 17th CIRP Conference on Modelling of Machining Operations, CIRP CMMO
PublisherElsevier
Pages444-449
Number of pages6
DOIs
Publication statusPublished - 5 Jul 2019
Event17th CIRP Conference on Modelling of Machining Operations - Sheffield, United Kingdom
Duration: 13 Jun 201914 Jun 2019

Publication series

NameProcedia CIRP
PublisherElsevier
Volume82
ISSN (Print)2212-8271

Conference

Conference17th CIRP Conference on Modelling of Machining Operations
Abbreviated titleCIRP CMMO
Country/TerritoryUnited Kingdom
CitySheffield
Period13/06/1914/06/19

Keywords

  • artificial neural networks
  • intelligent/smart manufacturing
  • manufacturing informatics
  • multistage manufacturing
  • UKRI
  • EPSRC
  • EP/P006930/1

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