Meeting Resources
TMS Learning Pathways: Advanced Materials Manufacturing

December 7–9, 2020

Course Curriculum

Taylor Sparks

"I think materials informatics is poised to dramatically accelerate materials discovery. It’s important for materials researchers to be on the cutting edge of data science tools and techniques."

—Taylor Sparks, Associate Professor and Associate Chair, Materials Science and Engineering Department, University of Utah

David Matlock

"The last several years have been exciting times in the global steel industry as new innovative steels, developed to enable economic lightweight vehicle designs, have evolved and are now in the marketplace. This TMS program offers the opportunity share some of these developments with the broader materials community."

—David Matlock, Emeritus Professor, George S. Ansell Department of Metallurgical and Materials Engineering, Colorado School of Mines

Course Structure

This three-day virtual event offers concurrent short courses that are modularly assembled within the following learning tracks: machine learning, additive manufacturing, and lightweighting. Course modules range from introductory to advanced levels for each of the three tracks. Access all modules for one price for a custom education experience.

Program Schedule Structure

Download the program schedule for an overview of the topics that you will learn. Learn more about the experts teaching the tracks on the Instructors page. Begin planning your professional development customization on the Registration page. 

Program Schedule

Monday, December 7, 2020

Sessions MACHINE LEARNING ADDITIVE MANUFACTURING LIGHTWEIGHTING
Monday Morning
10:00 a.m. to 
12:30 p.m. ET
Introduction to Machine Learning,
Session 1: Overview, Tools, Frameworks, Application to Materials Science
Instructor: Taylor Sparks
(introductory level)
 Introduction to Additive Manufacturing
Instructor: Wei Xiong
(introductory level)
 Introduction to Lightweighting
Instructor: Alan Luo
(introductory level)
Monday Afternoon
1:30 p.m. to 
4:00 p.m. ET
 Introduction to Machine Learning,
Session 2: Knowledge Extraction, Feature Engineering, Best Practices
Instructor: Taylor Sparks
(introductory level)
 Additive Manufacturing in Aerospace Engineering
Instructor: Eliana Fu
(introductory level)
 Aluminum Alloys for Lightweighting
Instructor: Kevin Anderson
(intermediate level)

Tuesday, December 8, 2020
Tuesday Morning
10:00 a.m. to 
12:30 p.m. ET 		Data Science and Alloy Design
Instructor: Dongwon Shin
(introductory level)
 Processing Optimization in Additive Manufacturing
Instructor: Michael Groeber
(intermediate level)
 Advanced Polymer Composites for Lightweighting
Instructor: Uday Vaidya
Tuesday Afternoon
Machine Learning and Lightweighting Sessions: 1:30 to 4:00 ET; Additive Manufacturing Session: 1:00 p.m. to 
3:30 p.m. ET
 Hands-on Session on ASCENDS
Instructor: Dongwon Shin
(introductory level)
 Standards Development and Data Management in Additive Manufacturing
Note: 1 p.m. start time
Instructor: Mohsen Seifi
(intermediate level)
 Titanium Alloys for Lightweighting
Instructor: Adam Pilchak
(combination of all levels)

Wednesday, December 9, 2020
Wednesday Morning
10:00 a.m. to 
12:30 p.m. ET		 Optimal Experimental Design in Materials Science and Engineering
Instructor: Gilad Kusne
(intermediate/adv level)
 Feedstock and Post-processing of Additive Manufacturing
Instructor: Todd Palmer
(intermediate level)
 Advanced High Strength Steels for Lightweighting
Instructor: David Matlock
(intermediate level)
Wednesday Afternoon
1:30 p.m. to 
4:00 p.m. ET
 Crosscutting Session for All Three Tracks
Moderator: Paul Mason, Thermo-Calc Software Inc.
Instructors: Michael Groeber, The Ohio State University; Edward Herderick, The Ohio State University; and Natasha Vermaak, Lehigh University

Learning Tracks

Machine Learning

Participants will learn different frameworks of data analytics, machine learning, and artificial intelligence that can be used to discover and exploit process-structure-property relationships in materials.

Participants will be exposed to some basic concepts in machine learning and hands-on data analytics exercises on realistic materials data sets, as well as advanced topics on the use of the latest technology to carry out optimal experimental design in materials. Learning objectives include:
  • Create, critically evaluate, and interpret machine learning models for materials and assess how machine learning tools can impact your work
  • Organize and curate data in a way that is suitable for machine learning
  • Familiarize with, and deploy data analytics tools on realistic materials datasets to understand (experimental) factors that control materials behavior
  • Design an experiment that incorporates machine learning

Machine Learning Modules

Introduction to Machine Learning, Session 1: Overview, Tools, Frameworks, Application to Materials Science – Monday Morning Session
Introductory Level
This session will cover an overview of machine learning for materials science. The session will emphasize answers to the following questions: “Why is machine learning important?,” “What has it accomplished so far?,” “Where does machine learning fail?,” and “What is the future of machine learning in materials science?” The session will not focus on how to best carry out machine learning in materials science. That will be covered in the next session. Case studies and summaries will be provided across a variety of fields including materials property prediction, structure classification, image segmentation, deep learning, and more.

Introduction to Machine Learning, Session 2: Knowledge Extraction, Feature Engineering, Best Practices – Monday Afternoon Session
Introductory Level
This session will expand on how machine learning enabled the successes described in the prior session. The first part of the session will be a tutorial explanation of detailed best practices for new practitioners. Specifically, we will cover data discovery, curation, and processing, train-test-splits, featurization, algorithm selection, model deployment, and validation. The second part of the session will be a high-level explanation of some of the state-of-the-art techniques in materials informatics including feature engineering vs feature-free deep learning, image segmentation, natural language processing, structure vs composition-based featurization, active learning, and data visualization.

Data Science and Alloy Design

Hands-on Session on ASCENDS
Introductory Level
The two sessions on Day 2 will cover the basics of data science needed for applying machine learning and correlation analysis in alloy design. ORNL’s ASCENDS will be used for a hands-on session with examples of predicting mechanical properties of high-temperature alloys.

Optimal Experimental Design in Materials Science and Engineering
Intermediate/Advanced Level
In this session, we will introduce Gaussian processes and their use for active learning/machine learning guided experiment design. This session will be hands on using python in Colab. If you are interested in following along, please bring a laptop. We will also discuss some recent implementations of the GP-AL pairing for materials exploration and discovery.

Additive Manufacturing

Participants will gain key knowledge of common additive manufacturing processes and practices with an emphasis on understanding critical materials science and engineering problems and tools that can address them.

Learning objectives include:
  • Understand the fundamentals of materials used in additive manufacturing, with an emphasis on metals and alloys
  • Describe key processing and post-processing considerations for common addictive manufacturing applications
  • Explain qualification and certification best practices from industry and regulatory perspectives
  • Explore opportunities in industrial applications of additive manufacturing, e.g., aerospace engineering

Additive Manufacturing Modules

Introduction to Additive Manufacturing
Introductory Level
Additive manufacturing has captured the attention of the materials community, but many industries and institutions have not yet begun the implementation of an additive manufacturing strategy. For those new to additive manufacturing, this session is the best place to start. Materials-related topics in additive manufacturing will be discussed.

Additive Manufacturing in Aerospace Engineering
Introductory Level
Additive manufacturing applications in aerospace engineering will be introduced with the development of materials, in-situ melting/sintering, as well as post-processing. The pathways from concept to final products will be discussed by taking the applications in aerospace engineering and space exploration.

Processing Optimization in Additive Manufacturing
Intermediate Level
Familiar with additive manufacturing but looking for more detail on the processing optimization in additive manufacturing; as well as future opportunities for microstructure engineering for special additive manufacturing processes such as laser melting.

Standards Development and Data Management in Additive Manufacturing
Intermediate Level The necessity of standards development and their impact on additive manufacturing applications will be discussed. In addition, the opportunities for additive manufacturing related to data informatics of process- structure-property relationships will be addressed.

Feedstock & Post-Processing of Additive Manufacturing
Intermediate Level
Feedstock topics related to raw materials composition and its consequent microstructure engineering in post-processing will be introduced. Case studies will be performed to understand the composition- processing-microstructure-property relationships in additive manufacturing technique development.

Lightweighting

Participants will strengthen their understanding of the fundamental elements of materials engineering design, properties, and manufacturing processes and how they can be used in lightweighting applications.

Learning objectives include:
  • Develop/select lightweight materials (advanced high strength steels, light alloys, and polymer-based composites) for various automotive, aerospace, and maritime applications
  • Understand key relationships between processes, microstructure, and service properties in lightweighting applications
  • Explain how integrated computational materials engineering (ICME) can be leveraged for lightweight design and applications
  • Understand mass/cost tradeoffs and opportunities with alternative materials, multi-material joining, and manufacturing

Lightweighting Modules

Introduction to Lightweighting
Introductory Level
This module will focus on lightweighting strategy and technologies, including material selection, design, and manufacturing innovations. Participants will examine lightweighting case studies in automotive subsystems and will explore the use of magnesium alloys for lightweighting.

Aluminum Alloys for Lightweighting
Intermediate Level
Aluminum is a low cost, highly available, lightweight metal that has a wide range of commercial application. In the session, the instructor will present several lightweighting concepts that are pertinent to aluminum. He will also discuss numerous product forms and how they can assist in achieving lightweight designs in real-world products.

Advanced Polymer Composites for Lightweighting
Check back soon for module description.

Titanium Alloys for Lightweighting
Combination of All Levels
This session will focus on the physical metallurgy of titanium and titanium alloys. An emphasis will be placed on the mechanisms of microstructure and texture evolution during processing and its impact on material behavior. These will be highlighted through case studies relevant to current issues in the titanium industry, including abnormal grain growth and dwell fatigue failure.

Advanced High Strength Steel for Lightweighting
Intermediate Level
Significant recent developments leading to new advanced high strength steels to enable lightweight vehicle designs will be presented. The discussion will emphasize sheet steels but will also consider advances in bar and forging steels as well as new manufacturing processes designed to optimize steel microstructure and property combinations.

Crosscutting Session

The modules in the crosscutting session will be applicable to all three learning tracks.

Design and Lightweighting via Topology Optimization
Crosscutting module taught by Natasha Vermaak
Topology optimization (TO) offers a mathematical framework to determine the most efficient material layout for prescribed constraints and loading conditions. This presentation will review TO basics, suggest educational resources, and highlight an example of exploiting TO at the intersection of multifunctional design and lightweighting.

Perspectives on Additive Manufacturing and Opportunities for Materials Research
Crosscutting module taught by Edward D. Herderick
New digital manufacturing tools have captured the imagination of product designers across the industrial landscape. Additive manufacturing is the poster child, and there are similarly exciting developments in robotics as well as “conventional” manufacturing methods. This presentation will cover activities in Additive Manufacturing at the OSU Center for Design and Manufacturing Excellence (CDME) with an emphasis on industrially focused problems and will conclude with a perspective on exciting frontiers for future materials research and development opportunities.

For More Information

For more information about this meeting, please complete the meeting inquiry form or contact:

TMS Meeting Services
5700 Corporate Drive Suite 750
Pittsburgh, PA 15237
Telephone:
U.S. and Canada Only: 1-800-759-4867
Other Countries: 1-724-776-9000, ext. 241
Fax: 1-724-776-3770