Skip to main content Accessibility help
×
Home

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

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.

Copyright

References

Hide All
1.Rangarajan, A., Radhakrishnan, P., Moitra, A., Crapo, A., Robinson, D., “Manufacturability Analysis and Design Feedback System Developed Using Semantic Framework,” Proc. ASME 2013 Int. Des. Eng. Tech. Conf. (IDETC) and Comput. Inf. Eng. Conf. (CIE) (2013).
2.SADL Semantic Application Design Language (open source), http://sadl.sourceforge.net.
3.Crapo, A., Moitra, A., Int. J. Semant. Comput. 7 (3), 215 (2013).
4.Moitra, A., Crapo, A., Palla, R., “Concept-Level Rules for Capturing Domain Knowledge,” 12th IEEE International Conference on Semantic Computing , Laguna Hills, CA, 2018.
5.Moitra, A., Palla, R., Rangarajan, A., “Automated Capture and Execution of Manufacturability Rules Using Inductive Logic Programming,” Innovative Applications of Artificial Intelligence (IAAI), Phoenix, 2016.
6.Nie, P., Ojo, O.A., Li, Z., Acta Mater . 77, 85 (2014).
7.Dheeradhada, V.S., Chennimalai Kumar, N., Dial, L., Hanlon, T., Vinciquerra, J., Gupta, V., “Machine Learning Assisted Development in Additive Manufacturing,” Patent Application 16/184,481 (2018).
8.Dheeradhada, V.S., Chennimalai Kumar, N., Dial, L., Hanlon, T., Vinciquerra, J., Gupta, V., Grande, J., “Machine Learning Assisted Parameter Development in Additive Manufacturing,” paper presented at the Materials Research Society Spring Meeting, Phoenix, 2018.
9.Kumar, N.C., Subramaniyan, A.K., Wang, L., “Improving High-Dimensional Physics Models through Bayesian Calibration with Uncertain Data,” ASME Turbo Expo 2012: Turbine Technical Conference and Exposition (American Society of Mechanical Engineers, Copenhagen, Denmark, 2012), pp. 407416.
10.Kumar, N.C., Subramaniyan, A.K., Wang, L., Wiggs, G., “Calibrating Transient Models with Multiple Responses Using Bayesian Inverse Techniques,” ASME Turbo Expo 2013: Turbine Technical Conference and Exposition (American Society of Mechanical Engineers, San Antonio, 2013).
11.Ling, Y., Ghosh, S., Asher, I.M., Kristensen, J., Ryan, K., Wang, L., “An Intelligent Sampling Framework for Multi-Objective Optimization in High Dimensional Design Space,” 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Kissimmee, FL, 2018.
12.Kristensen, J., Ling, Y., Asher, I., Wang, L., “Expected-Improvement-Based Methods for Adaptive Sampling in Multi-Objective Optimization Problems,” in ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, Charlotte, NC, 2016.
13.Ryan, K.M., Kristensen, J., Ling, Y., Ghosh, S., Asher, I., Wang, L., “A Gaussian Process Modeling Approach for Fast Robust Design with Uncertain Inputs,” ASME. Turbo Expo: Power for Land, Sea, and Air, Volume 7A: Structures and Dynamics.
14.Suzuki, A., Shen, C., Chennimalai Kumar, N., MRS Bull . 44 (4), 247 (2019).
15.Ajdelsztajn, L., Schoenung, J.M., Jodoin, B., Kim, G.E., Metall. Mater. Trans. A, 36 (3), 657 (2005).
16.Ajdelsztajn, L., Lavernia, E.J., Jodoin, B., Richer, P., Sansoucy, E., J. Therm. Spray Technol. 15 (4), 495 (2006).
17.Tlusty, J., Manufacturing Process and Equipment (Prentice Hall, Upper Saddle River, NJ, 2000), p. 463.
18.Taylor, F., Trans. ASME 28, 31 (1906).
19.Alauddin, M., El Baradie, M., Hashmi, M., J. Mater. Process. Technol. 71 (3), 456 (1997).
20.Usui, E., Shirakashi, T., Kitagawa, T., Wear 100 (1–3), 129 (1984).
21.Xie, L., Schmidt, J., Schmidt, C., Biesinger, F., Wear 258 (10), 1479 (2005).
22.Karandikar, J., Trans. NAMRI/SME 47 (forthcoming) (2019).
23.Bishop, C.M., Pattern Recognition and Machine Learning (Springer, Singapore, 2006).
24.Karandikar, J., Kurfess, T., J. Manuf. Syst. 37, 479 (2015).
25.Ronneberger, O., Fischer, P., Brox, T., “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Lecture Notes in Computer Science, vol. 9351; Navab, N., Hornegger, J., Wells, W., Frangi, A., Eds. (Springer, Cham, Switzerland, 2015).
26.Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., Zhang, Z., CoRR (2015), abs/1512.01274.
27.Arganda-Carreras, I., Kaynig, V., Rueden, C., Eliceiri, K.W., Schindelin, J., Cardona, A., Seung, H.S., Bioinformatics 33 (15), 2424 (2017).
28.Gugel, L., Brada, R.S., Santamaria-Pang, A., Wein, R., Wilson, G.S., “Multi-stage Segmentation Using Synthetic Images,” US Patent Application No. 16/229, 743, General Electric Company (March 12, 2019).
29.Kellner, T., Bringing Back the Bling: New Process Recovers Precious Platinum from “Smut,” GE Reports (March 31, 2015), https://www.ge.com/reports/post/115132114280/bringing-back-the-bling-new-process-recovers/.
30.Li, P., Wei, B., Xu, H., Gleason, M., Allison, W., Wessels, J., Zurawka, J., “Electro Discharge Machining Apparatus and Method,” US Patent US9333577B2.
31.Bian, X., Lim, S.N., Zhou, N., “Multiscale Fully Convolutional Network with Application to Industrial Inspection,” in Applications of Computer Vision (WACV), IEEE Winter Conference , 2016, pp.18.
32.Diwinsky, D.S., Lim, S.N., Bian, X., US Patent No. 9,785,919 (2017).
33.Maybury, M.T., Wahlster, W., Eds., Readings in Intelligent User Interfaces (Morgan Kaufman Publishers, San Francisco, 1998).
34.Castellanos, S., “Augmented Reality to Debut on GE’s Factory Floors,” CIO J ., (November 9, 2016), https://blogs.wsj.com/cio/2016/11/09/augmented-reality-to-debut-on-ges-factory-floors.
35.Kawamoto, M., Nishioka, K., Inui, T., Tsuchiya, F., J. Jpn. Soc. Test. Mater. 4 (19), 42 (1955).
36.Novovic, D., Dewes, R.C., Aspinwall, D.K., Voice, W., Bowen, P., Int. J. Mach. Tools Manuf. 44, 125 (2004).
37.As, S.K., Skallerud, B., Tveiten, B.W., Holme, B., Int. J. Fatigue 27, 1590 (2005).
38.Hanlon, T., Reimann, J., Soare, M.A., Singhal, A., Grande, J., Edgar, M., Aggour, K.S., Vinciquerra, J., CoRR, arXiv:1906.05270 [CS.LG] (2019).
39.Williams, J., Cuddihy, P., McHugh, J., Aggour, K.S., Menon, A., Gustafson, S., Healy, T., “Semantics for Big Data Access & Integration: Improving Industrial Equipment Design Through Increased Data Usability,” IEEE International Conference on Big Data (Big Data), 2015, Santa Clara, CA, pp. 11031112.
40.McHugh, J., Cuddihy, P.E., Williams, J.W., Aggour, K.S., Kumar, V.S., Mulwad, V., “Integrated Access to Big Data Polystores through a Knowledge-Driven Framework,” IEEE International Conference on Big Data (Big Data), 2017, Boston, pp. 14941503.
41.Cuddihy, P., McHugh, J., Williams, J.W., Mulwad, V., Aggour, K.S., “SemTK: A Semantics Toolkit for User-Friendly SPARQL Generation and Semantic Data Management,” 17th International Semantic Web Conference (ISWC), Industry and Blue Sky Ideas Track, Monterey, CA, 2018.
42.SemTK Semantics Toolkit (open source), https://github.com/ge-semtk/semtk.

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed