This four-part webinar series will explore the newest developments in artificial intelligence (AI) and how they affect the materials science and engineering (MS&E) fields. This series will cover several topics, including AI for materials design and processing, deep learning for materials and manufacturing innovation, autonomous research, and AI in manufacturing.

This webinar series consists of four sessions, from February 2 to February 11, 2021. Register once for access to all four webinars live or on demand.

What You Will Experience

• Provide a historical perspective and specific applications of AI in materials design and processing
• Discuss implementation of deep learning for materials and manufacturing innovations
• Cover key elements of employing AI for autonomous research
• Discuss challenges and approaches for machine learning in manufacturing

Registration

If you require a certificate of participation for attending this webinar, please email professionaldevelopment@tms.org.

Session 1 - Tuesday, February 2, 2021, 1-2 pm ET

Presenter

"Intelligent Software for Material Design"
Designing improved materials requires a comprehensive evaluation of data and model quality. With the volume, variety and rate of data generation continuously increasing, human analysis becomes extremely difficult, if not impossible. In this talk, the concept of “intelligent software” is discussed. The software includes elements of AI such as machine learning and computer vision, coupled with reduced-order modeling and Bayesian statistics. The value of the approach is illustrated using examples of material design and real-time optimization of manufacturing processes.

Bio
Marius Stan is a senior scientist and leader of Intelligent Materials Design in the Applied Materials Division at Argonne National Laboratory. He is also a senior fellow at University of Chicago and Northwestern University. Stan and his group use AI and high-performance, multi-scale computer simulations to understand and predict physical and chemical properties of multi-component metals and ceramics. The applications include energy production (nuclear fuels and reactor materials), energy storage (batteries) and electronics. The group also uses AI to optimize complex processes for manufacturing applications such as 3D printing and flame spray pyrolysis. Stan has extensively published in the scientific literature, holds several patents, and is currently writing a book on modeling and simulation.

Moderator

Bio
Ankit Agrawal is a research professor in the Department of Electrical and Computer Engineering at Northwestern University. He specializes in interdisciplinary AI and big data analytics via high performance data mining, based on a coherent integration of high-performance computing and data mining to develop customized AI solutions for big data problems. His research has contributed to large-scale data-driven discoveries in various scientific and engineering disciplines, such as materials science, healthcare, social media, and bioinformatics. He has co-authored 150+ peer-reviewed publications, co-developed and released 15+ software, delivered four keynote and 40+ invited talks at major conferences, universities, and companies all over the world, been on program committees of 35+ conferences/workshops, and served as a PI/Co-PI on 15+ sponsored projects funded by various federal agencies (e.g., NSF, DOE, AFOSR, NIST, DARPA, DLA) as well as industry (e.g., Toyota Motor Corporation Japan). He is one of the few computer scientists who is actively introducing AI and advanced data science techniques in the field of materials science and has successfully led several large-scale materials informatics projects. As an example, he is co-leading the AI group at the Center for Hierarchical Materials Design (CHiMaD), which is a $60 million NIST-sponsored center of excellence. He is also serving as the editor-in-chief of Computers, Materials & Continua.

Session 2: Thursday, February 4, 2021, 1-2 pm ET

Presenter

"Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science"
Deep learning has emerged as a game-changing AI technology in the last few years, which has enabled numerous real-world applications, such as autonomous driving. In this talk, I will discuss the fundamentals of deep learning, along with some of our recent works at the intersection of deep learning and materials informatics, for exploring processing-structure-property-performance (PSPP) linkages in materials. The increasing availability of materials databases and big data in general, along with groundbreaking AI advances offer a lot of promise to accelerate the discovery, design, and deployment of next-generation materials.

Moderator

Session 3: Tuesday, February 9, 2021, 1-2 pm ET

Presenter

"Autonomous Research Systems for Materials Development"
Autonomous research systems are AI-driven research robots that promise to revolutionize materials, Research, and development. ARES™ demonstrated the first closed-loop experimentation to learn to control the synthesis of carbon nanotubes. Additive Manufacturing ARES™ is an autonomous 3D printer that taught itself to print structures using Bayesian optimization coupled with in-line image analysis. For this webinar, Maruyama will introduce autonomous research systems, their promise, and future directions.

Bio
Benji Maruyama is a principal materials research engineer in the Materials & Manufacturing Directorate of the Air Force Research Laboratory and the autonomous materials lead & ACT3 (Autonomous Capabilities Team 3) liaison. His focus area is the synthesis and processing science of carbon nanotubes using ARES™ which is the first fully Autonomous Research (ARES) Robot for materials. Maruyama’s interests include the research process itself, for which he promotes Moore’s Law for the speed of research. He is also the point of contact for carbon materials for the AFRL Materials & Manufacturing Directorate. His materials interests include carbon nanomaterials, energy storage, flexible-hybrid materials and processes, field emission, carbon, polymer and metal matrix composites, imaging of complex 3D microstructures and AI/Machine Learning. He is currently involved in the study of the origins of chiral growth for carbon nanotubes, defect engineering for low dimensional materials, catalysis and autonomous experimentation.

Moderator

Bio
David Blondheim, Jr. graduated in 2004 with his B.S. in Mechanical Engineering from Michigan Technological University. He began his engineering career at a CNC machine job shop. While working full time, he became a Professional Engineer (PE), completed his MBA from UW-Oshkosh in 2008 and obtained his M.S. in Industrial Engineering from Purdue University in 2012. After nine years of progressive experience in engineering for machining, he entered the die cast industry with Mercury Marine in 2013 where he serves as the Engineering Manager within the aluminum foundry and helps lead IIoT/Connected Operations initiatives throughout Mercury’s different manufacturing plants. David is currently a Ph.D. Candidate in Systems Engineering at Colorado State University working on his dissertation of improving die casting system process and data understanding with machine learning.

Session 4: Thursday, February 11, 2021, 1-2 pm ET

Presenter

"Challenges and Approaches for Machine Learning in Manufacturing"
Implementing machine learning within production manufacturing environments presents many challenges from data collection to small data sets to classification issues. Understanding these challenges and using different analytic approaches can make the difference in successful machine learning project implementations within manufacturing.

Moderator

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Learn more and make plans to participate in this exciting new event!

For More Information

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