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Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective

Published online by Cambridge University Press:  12 July 2019

Kareem S. Aggour ,
Vipul K. Gupta ,
Daniel Ruscitto ,
Leonardo Ajdelsztajn ,
Xiao Bian ,
Kristen H. Brosnan ,
Natarajan Chennimalai Kumar ,
Voramon Dheeradhada ,
Timothy Hanlon  and
Naresh Iyer
...Show all authors
Kareem S. Aggour
Affiliation:
GE Research, USA, [email protected]
Vipul K. Gupta
Affiliation:
GE Research, USA, [email protected]
Daniel Ruscitto
Affiliation:
GE Research, USA, [email protected]
Leonardo Ajdelsztajn
Affiliation:
GE Research, USA, [email protected]
Xiao Bian
Affiliation:
GE Research, USA, [email protected]
Kristen H. Brosnan
Affiliation:
GE Research, USA, [email protected]
Natarajan Chennimalai Kumar
Affiliation:
GE Research, USA, [email protected]
Voramon Dheeradhada
Affiliation:
GE Research, USA, [email protected]
Timothy Hanlon
Affiliation:
GE Research, USA, [email protected]
Naresh Iyer
Affiliation:
GE Research, USA, [email protected]
Jaydeep Karandikar
Affiliation:
GE Research, USA, [email protected]
Peng Li
Affiliation:
GE Research, USA, [email protected]
Abha Moitra
Affiliation:
GE Research, USA, [email protected]
Johan Reimann
Affiliation:
GE Research, USA, [email protected]
Dean M. Robinson
Affiliation:
GE Research, USA, [email protected]
Alberto Santamaria-Pang
Affiliation:
GE Research, USA, [email protected]
Chen Shen
Affiliation:
GE Research, USA, [email protected]
Monica A. Soare
Affiliation:
GE Research, USA, [email protected]
Changjie Sun
Affiliation:
GE Research, USA, [email protected]
Akane Suzuki
Affiliation:
GE Research, USA, [email protected]
Raju Venkataramana
Affiliation:
GE Research, USA, [email protected]
Joseph Vinciquerra
Affiliation:
GE Research, USA, [email protected]
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Abstract

At GE Research, we are combining “physics” with artificial intelligence and machine learning to advance manufacturing design, processing, and inspection, turning innovative technologies into real products and solutions across our industrial portfolio. This article provides a snapshot of how this physical plus digital transformation is evolving at GE.

Keywords

compositemetalcoatingpowder metallurgyoptical metallography
Type
The Machine Learning Revolution in Materials Research
Information
MRS Bulletin , Volume 44 , Issue 7: The Machine Learning Revolution in Materials Research , July 2019 , pp. 545 - 558
Copyright
Copyright © Materials Research Society 2019 

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