AI for Advanced Manufacturing

Robotics

Call for Abstract - due September 13 »

Symposium Co-Chairs

Keith Brown
Assistant Professor
Boston University

Grace Gu
Assistant Professor, University of California, Berkeley

Key Speakers

AI-enabled additive manufacturing platform for extreme environments
Grace Gu
Assistant Professor, University of California, Berkeley

Materials Informatics for Simultaneous Design of Alloy Chemistry and AM Process
Scott Broderick
Assistant Professor, University of Buffalo

Artificial intelligence and machine learning are already making an impact in the manufacturing sector. With AI products can be better designed, and designed to be more effectively manufactured. Processes can be continually monitored and optimized to cut down unplanned downtime. Enhanced data collection and AI-powered analytics can significantly increase efficiency, product quality and the employee safety.

Join us for this exciting symposium featuring the latest innovations and future directions for AI in advanced manufacturing.

Back to Top ↑ 2021 Symposium Sessions
	Monday October 18
1:30	AI for Characterization & Manufacturing
	Wednesday October 20
8:30	AI Track Keynote
10:30	AI for Advanced Materials Discovery & Design

2021 Symposium Program
	Monday October 18

1:30	AI for Characterization & Manufacturing	Baltimore 1
	Session chair: Greg Haugstad, University of Minnesota & Grace Gu, University of California, Berkeley
1:30	Materials Informatics for Simultaneous Design of Alloy Chemistry and AM Process S. Broderick, University of Buffalo, US
1:55	Machine Learning for In-Water Inspection of Submarine Hull Coatings M. An, J. Cipolla, A. Shakalis, B. Hiriyur, R. Tolimieri, Prometheus Inc., US
2:15	Gamma-Ray Raster Imaging with Robotic Data Collection, pp. 109-110 W. Wells, T. Aucott, M. Siddiqi, Savannah River National Laboratory, US
2:35	A New Method for Atmospheric Correction of Satellite Data D. Groeneveld, Advanced Remote Sensing, Inc., US
2:55	Real-Time Porosity Prediction for Metal Additive Manufacturing using Convolutional Neural Networks W. Young, S. Ho, S. Al Jufout, M. Mozumdar, M. Buchholz, W. Zhang, K. Dajani, California State University, Long Beach, US
3:15	Forecasting and Decision Impact Analysis from Ripple Effects of Behaviors B. Frutchey, BigBear.ai, US
3:35	Leveraging Machine Learning to Predict Public Transportation Arrival Times P. Reshetova, W. Ruzicka, EastBanc Technologies, US
	Wednesday October 20

8:30	AI Track Keynote	Woodrow Wilson D
	Session chair: Richard Ross, 3M Company & Peter Koenig, Procter & Gamble
8:30	Closed-loop autonomous combinatorial experimentation for streamlined materials discovery I. Takeuchi, University of Maryland, US
9:00	From atoms to emergent mechanisms with information bottleneck and diffusion probabilistic models P. Tiwary, University of Maryland, US
9:30	Deep Learning for Linking Chemical and Biological Space in Small Molecules and Macromolecules A. Shehu, George Mason University, US

10:30	AI for Advanced Materials Discovery & Design	Annapolis 2
	Session chair: Richard Ross, 3M Company & Peter Koenig, Procter & Gamble
10:30	The combination of data-driven and physics-based modeling with application in protein formulations J.G.E.M. Fraaije, P. Petris, Siemens Culgi, NL
10:55	Next Generation PCIe Network Fabric for Simulators and Performance Computing C.T. Reynolds, Technical Systems Integrators, US
11:15	AI-accelerated materials innovation: From Optoelectronics to Fluorescent Biomarkers C. Kreisbeck, Kebotix, US
11:35	Machine Learning for the Exploration of Nanomaterial Synthesis Parameter Spaces R. Sappington, B. Cornick, Epic Advanced Materials, US
11:55	A Self-Driving Laboratory for Accelerated Materials Discovery C.P. Berlinguette, J.E. Hein, A. Aspuru-Guzik, B.P. MacLeod, F.G.L. Parlane, University of British Columbia, CA