TechConnect World 2020
Co-Located with Nanotech 2020Co-Located with Nanotech 2020 SBIR/STTRSBIR/STTR AI TechConnectAI TechConnect
Nanotech 2020

AI for Advanced Manufacturing


Symposium Co-Chairs

Brent M. SegalBrent M. Segal
Director of Technology Collaboration and Commercialization
Lockheed Martin

Ishita ChakrabortyIshita Chakraborty
Mechanical Engineer & Data Scientist
Stress Engineering Services

Keith BrownKeith Brown
Assistant Professor, Boston University

Key Speakers

Keith BrownA Bayesian Experimental Autonomous Researcher for Mechanical Design
Keith Brown
Assistant Professor, Boston University

Ying LiuYing Liu
Reader in Intelligent Manufacturing, Fellow, Data Innovation Research Institute
Cardiff University, United Kingdom

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.

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Symposium Sessions

Wednesday July 1

10:30AI Design & Manufacturing
1:30AI for Manufacturing Inspection and Control
4:00Innovations in AI - Posters
4:00AI for Advanced Manufacturing - Posters

Symposium Program

Wednesday July 1

10:30AI Design & Manufacturing
Session chair: Ishita Chakraborty, Stress Engineering Services, US
A Bayesian Experimental Autonomous Researcher for Mechanical Design
K. Brown, Boston University, US
Preform Design Prediction Model Based on Convolutional Neural Network Deep Learning in Piston Forging
S. Lee, L. Quagliato, J. Sun, N. Kim, Sogang University, KR
Semi-supervised and Reinforcement Learning Methods for VLSI Chip Design
J. Obert, Sandia National Labs, US
A Deep Convolutional Neural Network for Predicting the Failure Response of High-pressure Gas Pipes subject to Pitting Corrosion
S. Soghrati, Ohio State University, US
A Holistic, Digital-Twin Approach to Performance Simulation of Automated Fiber Placement Manufactured Parts
R. Cook, P.-Y. Lavertu, MSC Software, US
Machine Learning for Glass Manufacturing
M. Bauchy, University of California, Los Angeles, US
1:30AI for Manufacturing Inspection and Control
Session chair: Keith Brown, Boston University, US
Y. Liu, Cardiff University, UK
Manufacturing Quality Inspection Using AI and Edge Computing
C. Ouyang, T. Cook, C. Lu, IBM, US
Artificial Intelligence and Machine Learning for Weld Modeling and Quality Monitoring
J.E. Jones, V.L. Rhoades, M.D. Mann, T. Surrufka, EnergynTech, Inc., US
A Data-Driven Approach for Selecting Critical Process Parameters in Material Extrusion Additive Manufacturing
F. Pourkamali-Anaraki, A. Peterson, R. Jensen, University of Massachusetts Lowell, US
Laser Dissimilar Material Quality Assessment by Deep Learning
T. Kim, C. Han, H. Choi, Keimyung University, KR
Semantic Segmentation for 3D Feature Detection in the Automation of High Mix Industrial Processes
M. Powelson, Southwest Research Institute, US
Machine Learning for In-Water Inspection of Submarine Hull Coatings
M. An, J. Cipolla, A. Shakalis, B. Hiriyur, R. Tolimieri, Prometheus Inc., US
Gamma-Ray Raster Imaging with Robotic Data Collection
W. Wells, T. Aucott, Savannah River National Laboratory, US
4:00Innovations in AI - Posters
An EHR using AI technology as a Clinical Decision Support Tool
J.M. Penn, Guidance Founation Inc., US
IoT + DDoS = Disruptive (Business + Cyber) Risk!
A. Pabrai, ecfirst, US
Improve Health Outcomes and Maximize Quality Improvement: Using Artificial Intelligence Models
V. Melenez, HealthEC, US
A Review of AI Influence in Intellectual Property Law
D. Mottley, Howard University, School of Law, US
ABSCA - Boost Converter Switching Controller using Machine Learning Algorithms
B. Abegaz, Loyola University of Chicago, US
AROSV - An ROS based Self-Driving Vehicle Controller using Unsupervised Machine Learning Methods
B. Abegaz, Loyola University of Chicago, US
Dropping 500 Feet in 20 Seconds: Simulating the Cockpit Experience of an Airliner with a Trim Control Failure
A. Redei, Central Michigan University, US
Artificial Intelligence Trends Based on the Patents Granted by the United States Patent and Trademark Office
H.H.N. Abadi, M. Pecht, University of Maryland - Center for Advanced Life Cycle Engineering (CALCE), US
A Survey of Artificial Intelligence Funding in China
Z. He, W. Diao, M.G. Pecht, University of Maryland, US
Next Generation PCIe Network Fabric for High Performance AI Computing
C. Reynolds, Technical Systems Integrators, US
AI - Lack of data characterization significantly reduces accuracy of AI results
M. Gilger, Modus Operandi, US
4:00AI for Advanced Manufacturing - Posters
Residual Distortion Prediction through an Artificial Intelligence Approach in Additive Manufactured Components
A. Imanian, TDA, US
Bayesian Networks Connecting Processing and Product Features in Additive Manufacturing
A. Malmberg, K. Chandra, A. Peterson, J. Mead, University of Massachusetts Lowell, US
Using Robotics to Assemble Graphene Supercapacitor
C. Wu, J. Kim, D. Magluyan, D.K. Kindred, Y. Zhou, N. Cao, H. Zhao, Z. Kuang, T. Kidd, S. Dobbs, Z. Yu, California State Polytechnic University, Pomona, US
2020 Sponsors & Partners

2019 SBIR/STTR Agency Partners:

SBIR/STTR Agency Partners