TY - GEN
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
UR - http://www.scopus.com/inward/record.url?scp=85070457891&partnerID=8YFLogxK
UR - https://eprints.whiterose.ac.uk/148526/
U2 - 10.1016/j.procir.2019.04.148
DO - 10.1016/j.procir.2019.04.148
M3 - Conference contribution
AN - SCOPUS:85070457891
T3 - Procedia CIRP
SP - 444
EP - 449
BT - Proceedings of the 17th CIRP Conference on Modelling of Machining Operations, CIRP CMMO
PB - Elsevier
T2 - 17th CIRP Conference on Modelling of Machining Operations
Y2 - 13 June 2019 through 14 June 2019
ER -