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

被引：41

作者：

Aggour, Kareem S. ^{[1
]}

Gupta, Vipul K. ^{[2
]}

Ruscitto, Daniel ^{[3
]}

Ajdelsztajn, Leonardo ^{[2
]}

Bian, Xiao ^{[4
]}

Brosnan, Kristen H. ^{[2
]}

Kumar, Natarajan Chennimalai ^{[5
]}

Dheeradhada, Voramon ^{[2
]}

Hanlon, Timothy ^{[2
]}

Iyer, Naresh ^{[6
]}

Karandikar, Jaydeep ^{[7
]}

Li, Peng ^{[7
]}

Moitra, Abha ^{[1
]}

Reimann, Johan ^{[6
]}

Robinson, Dean M. ^{[8
]}

Santamaria-Pang, Alberto ^{[4
]}

Shen, Chen ^{[2
]}

Soare, Monica A. ^{[2
]}

Sun, Changjie ^{[5
]}

Suzuki, Akane ^{[2
]}

Venkataramana, Raju ^{[9
]}

Vinciguerra, Joseph ^{[10
]}

机构：

[1] GE Res, AI Knowledge & Big Data Grp, Niskayuna, NY 12309 USA

[2] GE Res, Struct Mat Grp, Niskayuna, NY USA

[3] GE Res, Mat Characterizat Grp, Niskayuna, NY USA

[4] GE Res, AI & Comp Vis Grp, Niskayuna, NY USA

[5] GE Res, Mech & Design Grp, Niskayuna, NY USA

[6] GE Res, AI & Machine Learning Grp, Niskayuna, NY USA

[7] GE Res, Struct Mat Mfg Grp, Niskayuna, NY USA

[8] GE Res, Addit Mfg Technol Grp, Niskayuna, NY USA

[9] GE Res, Human Comp Interact Grp, Niskayuna, NY USA

[10] GE Res, Addit Platform, Niskayuna, NY USA

来源：

MRS BULLETIN | 2019年 / 44卷 / 07期

关键词：

composite; metal; coating; powder metallurgy; optical metallography; PREDICTION; MODELS; LIFE;

D O I：

10.1557/mrs.2019.157

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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. © 2019 Materials Research Society.

引用

页码：545 / 558

页数：14

共 40 条

[1] Cold spray deposition of nanocrystalline aluminum alloys [J].

Ajdelsztajn, L ;

Jodoin, B ;

Kim, GE ;

Schoenung, JM .

METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE, 2005, 36A (03) :657-666

[2] Cold gas dynamic spraying of iron-base amorphous alloy [J].

Ajdesztajn, L. ;

Jodoin, B. ;

Richer, P. ;

Sansoucy, E. ;

Lavernia, E. J. .

JOURNAL OF THERMAL SPRAY TECHNOLOGY, 2006, 15 (04) :495-500

[3] Prediction of tool life in end milling by response surface methodology [J].

Alauddin, M ;

ElBaradie, MA ;

Hashmi, MSJ .

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 1997, 71 (03) :456-465

[4]

[Anonymous], 2016, IEEE WACV

[5] Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification [J].

Arganda-Carreras, Ignacio ;

Kaynig, Verena ;

Rueden, Curtis ;

Eliceiri, Kevin W. ;

Schindelin, Johannes ;

Cardona, Albert ;

Seung, H. Sebastian .

BIOINFORMATICS, 2017, 33 (15) :2424-2426

[6] Fatigue life prediction of machined components using finite element analysis of surface topography [J].

Ås, SK ;

Skallerud, B ;

Tveiten, BW ;

Holme, B .

INTERNATIONAL JOURNAL OF FATIGUE, 2005, 27 (10-12) :1590-1596

[7]

BISHOP C. M., 2006, Pattern recognition and machine learning, DOI [DOI 10.1117/1.2819119, 10.1007/978-0-387-45528-0]

[8]

Castellanos S., 2016, CIO J 1109

[9] High Prevalence of Assisted Injection Among Street-Involved Youth in a Canadian Setting [J].

Cheng, Tessa ;

Kerr, Thomas ;

Small, Will ;

Dong, Huiru ;

Montaner, Julio ;

Wood, Evan ;

DeBeck, Kora .

AIDS AND BEHAVIOR, 2016, 20 (02) :377-384

[10] TOWARD A UNIFIED ENGLISH-LIKE REPRESENTATION OF SEMANTIC MODELS, DATA, AND GRAPH PATTERNS FOR SUBJECT MATTER EXPERTS [J].

Crapo, Andrew ;

Moitra, Abha .

INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING, 2013, 7 (03) :215-236

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