Solid-state metal additive manufacturing

Frank Pfefferkorn, University of Wisconsin-Madison

 Biography

Dr. Frank Pfefferkorn is a Professor and the Associate Chair for Graduate Studies in the Department of Mechanical Engineering and the Director of the Manufacturing Systems Engineering Program at the University of Wisconsin-Madison. His Doctoral Degree is in Mechanical Engineering from Purdue University in West Lafayette, IN (2002). His core expertise is in the experimental and numerical investigation of discrete metal part manufacturing process physics. Dr. Pfefferkorn's research focuses on where the tool meets the workpiece, whether that tool is a mechanical cutting tool, laser beam, or friction stir tool. He has conducted advanced manufacturing process research for 29 years. He has active research projects in solid-state joining, laser polishing, instrumenting cutting tools, solid-state metal additive manufacturing, and multi-material additive-subtractive manufacturing. Dr. Pfefferkorn has authored over 165 peer-reviewed publications in these areas, including journal articles, conference proceedings, and invited book chapters. His research has been funded by the US National Science Foundation, US Department of Energy, US Office of Naval Research, Wisconsin Alumni Research Foundation, Machine Tool Technology Research Foundation, Austrian Marshall Plan Foundation, and industry.

 Tutorial description

This tutorial describes the deposition (printing) of metal using processes in which the material does not exceed the melting point. This is achieved by hot working the metal: temperatures are usually between 70% and 95% of the solidus temperature. Metal is deformed and bonded to the substrate by utilizing friction, pressure, velocity, and time. The severe plastic deformation during deposition results in a fine-grained microstructure. The dynamic recrystallization and lower temperatures and temperatures gradients, compared with melting-based processes, results in less formation of intermetallic phases, oxides, and residual stresses. It must also be noted that the hot working nature of the processes results in large forces and torques (at least locally). Significant advantages of these processes are their ability to deposit almost any metal alloy, create deposits/bonds between dissimilar materials, and achieve high deposition rates. The solid-state additive manufacturing processes are still in the early stages of adoption and this tutorial aims at providing a foundation of information that will enable the attendee to begin the process of evaluating these processes for their application(s) and pursue additional sources to increase their knowledge.

The following topics are covered in detail:

• description of process physics
• benefits and limitations
• comparison to melting-based metal additive manufacturing (e.g., economics, properties of build)
• description of various methods (e.g., friction surfacing, friction stir additive, ultrasonic, cold gas spraying).

Applications, future potential, examples of ongoing research, and companies providing commercial solutions are included.

Metallurgy of additive manufacturing: Towards born qualified parts

Sudarsanam Suresh Babu, University of Tennessee, Knoxville/Oak Ridge National Laboratory

Biography

Dr. Suresh Babu obtained his bachelor’s degree in metallurgical engineering from PSG College of Technology, Coimbatore, India, and his master’s degree in industrial welding metallurgy-materials joining from Indian Institute of Technology, Madras. He obtained his PhD in materials science and metallurgy from University of Cambridge, UK in 1992. He also worked as a research associate in the prestigious Institute for Materials Research, Sendai, Japan before joining ORNL in 1993.  From 1993 to 1997, he held joint researcher position with ORNL, University of Tennessee, and Penn State University.  From 1997 to 2005, he worked as an R&D staff at ORNL.  From 2005 to 2007, Suresh held a senior level technology leader position in engineering and materials at the Edison Welding Institute, Columbus, OH. From 2007 to 2013, Suresh served as Professor of Materials Science and Engineering and Director of NSF I/UCRC Center for Materials Joining Science for Energy Applications, at The Ohio State University. In 2013, Suresh was appointed as UT/ORNL Governor’s chair of advanced manufacturing at the University of Tennessee, Knoxville, TN. In this role he acts as a bridge to the ORNL’s expertise and infrastructure including manufacturing demonstration facility to develop a collaborative research and education ecosystem locally and deploy engineering solutions to manufacturing industries. In 2019, Suresh was also appointed as Director of Bredesen Center for Interdisciplinary Research and Education for Energy- and Data- Science and Engineering. In 2020, Suresh was selected to be the founding educational director of the UT-Oak Ridge Innovation Institute.  In 2020, he was appointed to the National Science Board by the President of the USA for a six-year term.  In 2022, he was appointed as inaugural position of senior advisor for research and STEM to the Provost and Vice Chancellor of Research.

Tutorial description

This tutorial will describe the approaches to map the effect of large number of variables related to additive manufacturing (AM) to fundamental thermo-mechanical-chemical signatures. These signatures, in turn, control the microstructure, properties, and geometrical conformity of manufactured components. The ability to describe these transient behaviors using combination of modeling and ex-situ/in-situ characterization will be highlighted. Links to open domain computational models will be provided. The sequence of topics to be covered are:

• mechanisms behind complex thermo-mechanical-chemical signatures during both fusion and solid-state additive manufacturing
• integrated approach that combines computational modeling, data science tools and in-situ monitoring
• examples of published qualification pathways to arrive at site-specific properties and rationalization of scatter in mechanical properties
• unresolved gaps and challenges and potential pathways to solve them.

Control of manufacturing systems, machines, and processes in the context of Industry 4.0

Chinedum Okwudire, University of Michigan

Biography

Chinedum (Chi) Okwudire is a professor of Mechanical Engineering and Miller Faculty Scholar at the University of Michigan. Prior to joining Michigan, he was the mechatronic systems optimization team leader at DMG Mori USA, Davis, CA. His research is focused on exploiting knowledge at the intersection of machine design, control, and computing to boost the performance of manufacturing automation systems at low cost. Chi has received several awards including the CAREER award from the National Science Foundation; the Young Investigator Award from the International Symposium on Flexible Automation; the Outstanding Young Manufacturing Engineer Award from SME; the Ralph Teetor Educational Award from SAE International; and the Russell Severance Springer Visiting Professorship from UC Berkeley. He was recently selected by SME as one of the 25 leaders transforming manufacturing. He has co-authored several best-paper-award-winning papers in the areas of manufacturing automation, control, and mechatronics. He is also the founder and CTO of Ulendo Technologies, Inc., a start-up company focused on developing automation software for 3D printing and other manufacturing processes.

Tutorial description

The control of manufacturing systems, machines, and processes is being transformed by the technologies shaping the smart manufacturing (Industry 4.0) revolution. This tutorial will review key technologies, including the Internet of things (IoT), cloud computing, artificial intelligence/machine learning, and digital twins, that are driving smart manufacturing. Then it will provide industrial case studies and specific examples to show how participants can leverage these technologies to improve the quality, productivity, and/or cost effectiveness of manufacturing machines and processes through advanced control. The tutorial will be interactive and will not assume any prior background in control theory.

At the end of this tutorial, participants will be able to:

• identify major classes of control used in manufacturing and other industries and understand their importance
• appreciate key technologies shaping smart manufacturing and their relevance to manufacturing control
• identify industry-relevant cases where advanced control, supported by smart manufacturing technologies, is leading to significant performance improvements.

Machining dynamics: Theory and application

Tony Schmitz, University of Tennessee, Knoxville/Oak Ridge National Laboratory

Biography

Tony Schmitz received his BS in Mechanical Engineering from Temple University in 1993 and his PhD in Mechanical Engineering from the University of Florida (UF) in in 1999. Schmitz completed a post-doctoral appointment at the National Institute of Standards and Technology (NIST) and was then employed as a Mechanical Engineer from 1999-2002. Schmitz accepted an appointment in the UF Department of Mechanical and Aerospace Engineering in 2002 and joined the Mechanical Engineering Department at UNC Charlotte in 2011.

Dr. Schmitz joined the Mechanical, Aerospace, and Biomedical Engineering department at the University of Tennessee, Knoxville (UTK) in 2019 with a Joint Faculty position at the Oak Ridge National Laboratory (ORNL) Manufacturing Demonstration Facility. At UTK, he directs the Machine Tool Research Center (MTRC). His most recent appointment is Director of the Southeastern Advanced Machine Tools Network (SEAMTN), a consortium of companies, colleges and universities, national laboratories, non-profit organizations, and the Tennessee state government that seeks to strengthen the US industrial base by investing in machine tool research and development, education, workforce development, and supply chain support. He continues his manufacturing research in support of the US machine tool industry with an emphasis on machining dynamics, metrology, machine learning, and additive manufacturing.

Tutorial description

This tutorial describes the dynamics of machining processes, with a particular focus on milling. The tutorial covers the steps required to improve machining productivity through chatter avoidance and reduced surface location error (forced vibrations resulting in part geometric errors). The following topics are covered in detail:

•        modal analysis and experimental methods for frequency response functions
•      description of milling dynamics models, including force modeling, time domain simulation, stability map algorithm, and surface location error calculation.

Examples are included. A graphical user interface (GUI) is also provided that enables users to complete virtual milling experiments.

Bayesian optimization for manufacturing

Jaydeep Karandikar, ORNL

Biography

Dr. Jaydeep Karandikar is a Senior R&D Staff Member in the Intelligent Machine Tools group at Oak Ridge National Laboratory. His broad research interests include machining process modeling, monitoring, & optimization, and smart manufacturing. Prior to joining ORNL, he was a lead research engineer at GE Research, Niskayuna, NY. Dr. Karandikar has published more than 25 peer-reviewed journal papers, two book chapters, and holds two US patents. Dr. Karandikar is a member of the executive committee of the Manufacturing Engineering Division at ASME, and a Research Affiliate at CIRP. Dr. Karandikar has received several awards including the SME S.M. Wu Research Implementation Award, ORNL Innovation Award, and the SME Outstanding Young Manufacturing Engineer Award.  Dr. Karandikar earned his PhD in Mechanical Engineering from the University of North Carolina at Charlotte in 2013 where his research focused on the application of decision analysis to manufacturing.

Tutorial description

Bayesian optimization (BO) is a sequential adaptive sampling strategy for the global optimization of black-box functions. This tutorial will describe BO methods for process parameter development and optimization in manufacturing. The tutorial will cover the basics of Gaussian Process (GP) machine learning and different acquisition functions for BO sampling using an example dataset.  Acquisition functions for sequential sampling (one sample) and batch sampling (multiple samples) will be described. 

Example Python codes for GP regression and BO will be provided. The tutorial aims to enable a practitioner/researcher in manufacturing to understand and apply BO for efficient process parameter development. The tutorial will be interactive and will not assume prior knowledge in machine learning.

Carbon fiber composites: Key considerations in design, tooling, manufacturing, and machining

Uday Vaidya, University of Tennessee, Knoxville/Oak Ridge National Laboratory

Biography

Dr. Uday Vaidya serves as Director of the University of Tennessee, Knoxville (UTK) Fibers and Composites Manufacturing Facility (FCMF), he is the Chief Technology Officer for IACMI-The Composites Institute, and he is the UT-ORNL Governor’s Chair in Advanced Composites Manufacturing. The FCMF is funded in collaboration with IACMI, a subsidiary of Collaborative Composite Solutions managed by the University of Tennessee Research Foundation. Through IACMI and UTK’s collaboration, more than 10 students have worked as IACMI interns in the laboratory space. IACMI members and partners collaborate on projects, offering both industry and laboratory research experience for UTK engineering students.

Tutorial description

Carbon fibers and their composites are making major strides in aerospace, defense, transportation, wind, power, sporting equipment, and infrastructure. The tutorial will cover the various continuous and discontinuous carbon fiber composites, design methodology, material characteristics and property envelopes, tooling for (and with) carbon fiber composites, manufacturing processes (for various sectors), machining of carbon fiber composites, and post-validation and inspection techniques. The course will be tailored to the audience to maximize learning opportunities.