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

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

doi:10.1016/j.procir.2020.03.075

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 proceeding › Conference contribution

154 Downloads (Pure)

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 language	English
Title of host publication	Proceedings of the 53rd CIRP Conference on Manufacturing Systems
Publisher	Elsevier
Pages	473-478
Number of pages	6
DOIs	https://doi.org/10.1016/j.procir.2020.03.075
Publication status	Published - 22 Sept 2020
Event	53rd CIRP Conference on Manufacturing Systems - Chicago, United States Duration: 1 Jul 2020 → 3 Jul 2020

Publication series

Name	Procedia CIRP
Publisher	Elsevier
Volume	93
ISSN (Print)	2212-8271

Conference

Conference	53rd CIRP Conference on Manufacturing Systems
Abbreviated title	CMS 2020
Country/Territory	United States
City	Chicago
Period	1/07/20 → 3/07/20

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure

Keywords

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

Access to Document

10.1016/j.procir.2020.03.075

1-s2.0-S221282712030696X-mainFinal published version, 755 KBLicence: CC BY-NC-ND

https://cirp-cms2020.northwestern.edu/

Cite this

@inproceedings{d930d1bd57d3425f8fe8615341036db3,

title = "Development of a new machine learning-based informatics system for product health monitoring",

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.",

keywords = "artificial neural networks, manufacturing informatics, multiple linear regression, multistage manufacturing process, principal componet analysis, UKRI, EPSRC, EP/P006930/1",

author = "Moschos Papananias and Olusayo Obajemu 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} 2020 The Authors.; 53rd CIRP Conference on Manufacturing Systems, CMS 2020 ; Conference date: 01-07-2020 Through 03-07-2020",

year = "2020",

month = sep,

day = "22",

doi = "10.1016/j.procir.2020.03.075",

language = "English",

series = "Procedia CIRP",

publisher = "Elsevier",

pages = "473--478",

booktitle = "Proceedings of the 53rd CIRP Conference on Manufacturing Systems",

}

Papananias, M, Obajemu, O, McLeay, TE, Mahfouf, M & Kadirkamanathan, V 2020, Development of a new machine learning-based informatics system for product health monitoring. in Proceedings of the 53rd CIRP Conference on Manufacturing Systems. Procedia CIRP, vol. 93, Elsevier, pp. 473-478, 53rd CIRP Conference on Manufacturing Systems, Chicago, United States, 1/07/20. https://doi.org/10.1016/j.procir.2020.03.075

TY - GEN

T1 - Development of a new machine learning-based informatics system for product health monitoring

AU - Papananias, Moschos

AU - Obajemu, Olusayo

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: © 2020 The Authors.

PY - 2020/9/22

Y1 - 2020/9/22

N2 - 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.

AB - 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.

KW - artificial neural networks

KW - manufacturing informatics

KW - multiple linear regression

KW - multistage manufacturing process

KW - principal componet analysis

KW - UKRI

KW - EPSRC

KW - EP/P006930/1

UR - https://www.scopus.com/pages/publications/85092434311

UR - https://eprints.whiterose.ac.uk/166464/

U2 - 10.1016/j.procir.2020.03.075

DO - 10.1016/j.procir.2020.03.075

M3 - Conference contribution

AN - SCOPUS:85092434311

T3 - Procedia CIRP

SP - 473

EP - 478

BT - Proceedings of the 53rd CIRP Conference on Manufacturing Systems

PB - Elsevier

T2 - 53rd CIRP Conference on Manufacturing Systems

Y2 - 1 July 2020 through 3 July 2020

ER -

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Fingerprint

Cite this