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

Moschos Papananias; Thomas E. McLeay; Mahdi Mahfouf; Visakan Kadirkamanathan

doi:10.1016/j.procir.2019.04.148

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 proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 17th CIRP Conference on Modelling of Machining Operations, CIRP CMMO
Publisher	Elsevier
Pages	444-449
Number of pages	6
DOIs	https://doi.org/10.1016/j.procir.2019.04.148
Publication status	Published - 5 Jul 2019
Event	17th CIRP Conference on Modelling of Machining Operations - Sheffield, United Kingdom Duration: 13 Jun 2019 → 14 Jun 2019

Publication series

Name	Procedia CIRP
Publisher	Elsevier
Volume	82
ISSN (Print)	2212-8271

Conference

Conference	17th CIRP Conference on Modelling of Machining Operations
Abbreviated title	CIRP CMMO
Country/Territory	United Kingdom
City	Sheffield
Period	13/06/19 → 14/06/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

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

Access to Document

10.1016/j.procir.2019.04.148

1-s2.0-S2212827119307620-mainFinal published version, 859 KBLicence: CC BY-NC-ND

https://www.amrc.co.uk/pages/cirp-cmmo2019

Cite this

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title = "An intelligent metrology informatics system based on neural networks for multistage manufacturing processes",

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.",

keywords = "artificial neural networks, intelligent/smart manufacturing, manufacturing informatics, multistage manufacturing, UKRI, EPSRC, EP/P006930/1",

author = "Moschos Papananias and McLeay, \{Thomas E.\} and Mahdi Mahfouf and Visakan Kadirkamanathan",

note = "Funding Information: The authors gratefully acknowledge funding for this research from the UK Engineering and Physical Sciences Research Council (EPSRC) under Grant Reference: EP/P006930/1. Publisher Copyright: {\textcopyright} 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations; 17th CIRP Conference on Modelling of Machining Operations, CIRP CMMO ; Conference date: 13-06-2019 Through 14-06-2019",

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doi = "10.1016/j.procir.2019.04.148",

language = "English",

series = "Procedia CIRP",

publisher = "Elsevier",

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Papananias, M, McLeay, TE, Mahfouf, M & Kadirkamanathan, V 2019, An intelligent metrology informatics system based on neural networks for multistage manufacturing processes. in Proceedings of the 17th CIRP Conference on Modelling of Machining Operations, CIRP CMMO. Procedia CIRP, vol. 82, Elsevier, pp. 444-449, 17th CIRP Conference on Modelling of Machining Operations, Sheffield, United Kingdom, 13/06/19. https://doi.org/10.1016/j.procir.2019.04.148

An intelligent metrology informatics system based on neural networks for multistage manufacturing processes. / Papananias, Moschos; McLeay, Thomas E.; Mahfouf, Mahdi et al.
Proceedings of the 17th CIRP Conference on Modelling of Machining Operations, CIRP CMMO. Elsevier, 2019. p. 444-449 (Procedia CIRP; Vol. 82).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - An intelligent metrology informatics system based on neural networks for multistage manufacturing processes

AU - Papananias, Moschos

AU - McLeay, Thomas E.

AU - Mahfouf, Mahdi

AU - Kadirkamanathan, Visakan

N1 - Funding Information: The authors gratefully acknowledge funding for this research from the UK Engineering and Physical Sciences Research Council (EPSRC) under Grant Reference: EP/P006930/1. Publisher Copyright: © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations

PY - 2019/7/5

Y1 - 2019/7/5

N2 - 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.

AB - 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.

KW - artificial neural networks

KW - intelligent/smart manufacturing

KW - manufacturing informatics

KW - multistage manufacturing

KW - UKRI

KW - EPSRC

KW - EP/P006930/1

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M3 - Conference contribution

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BT - Proceedings of the 17th CIRP Conference on Modelling of Machining Operations, CIRP CMMO

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ER -

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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