Tutorial 2
IEEE CASE 2019 Tutorial – Additive Manufacturing (AM) Data Management, Analytics and Process Control
Abstract:
Data plays the most critical role in linking AM lifecycle and value chain activities and streamlining the AM development process. While the AM data from a single build is essential for establishing part traceability, when methodically collected, the full processing history of thousands of components can be mined to advance our understanding of AM processes and control the process variety. As manufacturers start to capture this full body of AM data, the management, analysis and use of the data for process control are considerable challenges because AM embodies all the 4 V's characteristics of Big Data - volume, velocity, variety, and veracity.
The objective of this half-day tutorial session is to bring attention from the automation community to the cutting edge additive manufacturing data management, data analytics and process control technology, as well as the research challenges to accelerate the adoption of AM as a production method. Specifically, we will have 3 presentations each delivered by a domain expert on:
AM Data landscape and management (Dr. Yan Lu, NIST)
AM Data analytics (Dr. Paul Witherell, NIST)
AM data-driven process control and engineering decision making (Prof. Kira Barton, University of Michigan)
Organizers: [Yan Lu], [Senior Research Scientist]
[National Institute of Standards and Technology, USA]
E-mail: [yan.lu@nist.gov]
Phone: +[1] – [3019758228]
Agenda:
1:30-2:45 AM Data landscape and management (Dr. Yan Lu, NIST)
2:45-3:30 AM Data analytics (Dr. Witherell, NIST)
3:30-4:00 Break
4:00-4:45AM data-driven process control and engineering decision making (Prof. Kira Barton, University of Michigan)
4:45-5:30 AM data analytics and process control research roadmap discussion (All)
List of topics and their descriptions:
AM data landscape, data models, databases and data management
AM data analytics, data fusion and metamodeling
AM process control and part qualification
AM data related standards.
List of speakers and their biographical sketch:
Name |
Organization |
Affiliation & Bio-sketch |
Kira Barton |
University of Michigan |
Dr. Kira Barton is an Associate Professor and Miller Faculty Scholar in the Department of Mechanical Engineering at the University of Michigan. Kira conducts research in modeling, sensing, and control for applications in advanced manufacturing and robotics, with specializations in Iterative Learning Control, smart manufacturing and micro-additive manufacturing. Kira is the recipient of an NSF CAREER Award in 2014, 2015 SME Outstanding Young Manufacturing Engineer Award, the 2015 University of Illinois, Department of Mechanical Science and Engineering Outstanding Young Alumni Award, the 2016 University of Michigan, Department of Mechanical Engineering Department Achievement Award, and the 2017 ASME Dynamic Systems and Control Young Investigator Award. |
Paul Witherell |
National Institute of Standards and Technology |
Dr. Paul W. Witherell is a Mechanical Engineer in the Life Cycle Engineering (LCE) Group of the Systems Integration Division (SID) of the Engineering Laboratory (EL) at the National Institute of Standards and Technology (NIST). His primary objectives at NIST are to develop and transfer knowledge to industry, including knowledge about information models for additive manufacturing and system level analysis. Research efforts aim to leverage engineering and information sciences to benefit design flexibility, cost, and cycle times in additive manufacturing. His specific job focus is on identifying integration and technology issues that promote industry acceptance of information models, product representation standards, and open architecture that will enable rapid design-to-product transformations. Dr. Witherell's primary areas of interest are Design for Additive Manufacturing, Digital Thread for Additive Manufacturing, Design Optimization, Knowledge Representation in Product Development, Ontology and Semantic Relatedness for Design and Manufacturing, and Sustainable Manufacturing. |
Yan Lu |
National Institute of Standards and Technology |
Dr. Yan Lu. is a member of the Systems Integration Division (SID) of the Engineering Laboratory (EL) at the National Institute of Standards and Technology. She works on Smart Manufacturing programs at NIST. In addition, she serves as a US expert for IEC TC65 and also serve on several ISA standards committee, including ISA 95 and 106. Her current research interest covers service oriented smart manufacturing and knowledge management for additive manufacturing system development and applications. Before joining NIST, she was the head of Grid Automation Research Group at Siemens Corporation, Corporate Technology (SCR) where she has led and successfully delivered more than 10 million dollars of research projects in the areas of survivable industrial automation systems, energy management and smart grid automation. Dr. Lu also worked for Seagate Research Center for two years on developing hard disk drive servo controller. |
List of topics and their descriptions:
AM data landscape, data models, databases and data management
AM data analytics, data fusion and metamodeling
AM process control and part qualification
AM data related standards.
Expected participants and their background:
Additive Manufacturing modeling, data analysis and control researchers
Manufacturing informatics, manufacturing data management and data analytics technology developers
Related workshops/tutorials in previous CASE/ICRA/IROS:
CASE 2015
CASE 2017
CASE 2018