Digital Design and Manufacturing Lab: Events
Upcoming Events
Abhishek Bolar's Masters Thesis Defense
Title: AUTOMATION OF A MULTI STAGE T JOINT ASSEMBLY OF STAMPED COMPONENTS AND PREDICTION OF PERFORMANCE PARAMETERS USING MACHINE LEARNING
Advisor: Prof. Jami Shah
Date/Time: July 7th, 2023 / 9:00 AM ET
Zoom Meeting ID: 97220194664 | Password: 528102
Past Events
Design and Manufacturing Seminar Series (Fall 2020)
Posted:
September 22, 2020
Scrum's Impact on Product Design Education
Dr. David Ullman
Oregon State University
September 25, 2020, 3:00 – 4:15 pm
Delivered via Zoom
https://osu.zoom.us/j/98148502450?pwd=N3JxY1o3NkNXSE9kOUxMajFTN3hkUT09
Meeting ID: 981 4850 2450
Password: 884887
One tap mobile
+14086380968,,98148502450#,,,,0#,,884887# US (San Jose)
+16699006833,,98148502450#,,,,0#,,884887# US (San Jose)
ABSTRACT
In this presentation, after a brief background and history, I will explain why Scrum is so important to engineering product design. SCRUM design process has found extensive success in software development. Rather than the top-down waterfall or stage-gate processes, Scrum embraces that design is about removing uncertainty and making robust decisions as a product evolves. In this talk I will introduce the Scrum process and discuss challenges in its use for hardware and systems design. These challenges are being successfully overcome in a wide range of industries and resulting in quicker time to market, improved product quality, and more motivated engineers. In the future Scrum will become part of what is taught at universities and will be how capstone projects are managed.
About the Speaker:
David G. Ullman is an internationally recognized expert on product design and decision making best practices. He earned his BS and MS from U. of Cincinnati and PhD from Ohio State. His industry experience includes Air Force Flight Dynamics Lab (1970–72), Battelle Memorial Institute (1972–74) and Oxygen Enrichment Co., (1982–84). He was on the faculty of Oregon State from 1984-2002 and currently Emeritus Professor. In 2002 he founded Robust Decisions Inc. Ullman first became interested in the “process” of design while a student at Ohio State, inspired by the late Professor Walter Starkey. He spent his career teaching others how to design products. He is the founder of the ASME Design Theory and Methodology Committee. He is the author of “The Mechanical Design Process”, a text used at many Universities to teach design. In 2018 he began to weave the Scrum process into his consulting and teaching materials.
Not Just a Room Full of Tools: How University Makerspaces are Transforming Engineers into Innovators
Dr. Julie Linsey
Georgia Institute of Technology
October 2, 2020, 3:00 – 4:15 pm
Delivered via Zoom
https://osu.zoom.us/j/93805364554?pwd=cmkxSWlVdnpLTUFlRXdSbEN1Zi9oQT09
Meeting ID: 938 0536 4554
Password: 850196
ABSTRACT
University makerspaces have been touted as a possible avenue for improving student learning, engagement, retention, and creativity. However, there have been very few studies that use empirical data to evaluate how these spaces are impacting the people using them. The results from a series of studies will be presented. The first study presents results from a five-year longitudinal study of the impact of university makerspaces on students at three different universities. The second study implemented semi-structured interviews to identify the different design competencies and learning types that are reported by students and to identify the learning pathways into and through makerspaces. Longitudinal data (makerspace involvement, idea generation effectiveness, engineering design self-efficacy, GPA, major retention, and demographic data) is collected from three universities to track students as they progress through the engineering curriculum. The findings show a positive correlation between engineering design self-efficacy and involvement in academic makerspaces. Students who are highly engaged in makerspaces also are better idea generators. Class projects that require student use of the makerspace also encourage students to become voluntary involved in makerspaces. As expected, students learn a tremendous amount in the makerspaces from the expected equipment use, prototyping, and learning to fail to the unexpected of communication, life-long learning skills, and learning by watching others. Current work and future work will also be discussed. Makerspace present a path for incorporating hands-on learning, manufacturing, tool knowledge and many other critical innovation skills into an already overpack curriculum.
About the Speaker:
Dr. Julie Linsey is an Associate Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technological. Her research area is design cognition, including systematic methods and tools for innovative design with a particular focus on concept generation and design-by-analogy. She also works on design methods, theory, and engineering education. The goal of Dr. Linsey’s research is to discover new knowledge about how engineers think and leverage this knowledge into design methods and engineering education. Her current work includes quantifying the impact of university maker spaces, determining the impact of AI sketch recognition tutoring system on Statics student, and identifying how learning to free-hand sketch facilitates improvements to spatial visualization. She has authored over 150 technical publications, including over forty journal papers, six book chapters, and she holds two patents.
Realizing Next Generation Additive Manufacturing through Use of Advanced Robotics
Dr. S. K. Gupta
University of Southern California
October 23, 2020, 3:00 – 4:15 pm
Delivered via Zoom
https://osu.zoom.us/j/96436294741?pwd=ZDZGclhxQUlFWDlNbnExYWJyZEd0QT09
Meeting ID: 964 3629 4741
Password: 193149
ABSTRACT
Additive Manufacturing (AM) is expected to revolutionize manufacturing. The current generation of AM technology has overcome many limitations of traditional manufacturing. However, the current AM technology still needs many improvements. This presentation will describe how robots can be used to realize the next generation of AM technologies. The first part of this presentation will describe how performing material deposition using articulated robot arms can significantly expand AM processes capabilities by enabling material deposition on non-planar layers. Many composite parts have thin three-dimensional shell structures. Achieving the right fiber orientation is critical to the functioning of these parts. Printing them using conventional planar-layer AM processes leads to fibers being oriented in the plane of the layer. The capability to deposit the material along non-planar conformal layers can produce parts with improved material properties. We have demonstrated that the use of non-planar layers can significantly improve structural performance. We have also demonstrated how robots can be used to perform multi-resolution printing that finds the best trade-off between build speed and surface finish. By using two collaborating robots we have demonstrated the feasibility of supportless additive manufacturing. In the near foreseeable future, AM is not expected to produce high-quality electronics (e.g., processor, sensors). We will describe how the use of robots enables the insertion of externally fabricated components such as sensors, actuators, and energy harvesting components during the AM process. The second part of this presentation will describe artificial intelligence techniques needed for generating and executing robot trajectories needed to build accurate parts using additive manufacturing.
About the Speaker:
Dr. Satyandra K. Gupta is Smith International Professor in the Department of Aerospace and Mechanical Engineering and Department of Computer Science in Viterbi School of Engineering at the University of Southern California. He serves as the Director of the Center for Advanced Manufacturing. He served as a program director for the National Robotics Initiative at the National Science Foundation from September 2012 to September 2014. Dr. Gupta's interests are in the area of physics-aware decision making to facilitate and advance the state of automation. He has published more than four hundred technical articles. He is a fellow of the American Society of Mechanical Engineers (ASME), Institute of Electrical and Electronics Engineers (IEEE), and Society of Manufacturing Engineers (SME). He serves as the editor of the ASME Journal of Computing and Information Science in Engineering. Dr. Gupta has received numerous honors and awards for his scholarly contributions. Representative examples include a Young Investigator Award from the Office of Naval Research in 2000, Robert W. Galvin Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers in 2001, CAREER Award from the National Science Foundation in 2001, Presidential Early Career Award for Scientists and Engineers in 2001, Invention of the Year Award at the University of Maryland in 2007, Kos Ishii-Toshiba Award from ASME in 2011, Excellence in Research Award from ASME Computers and Information in Engineering Division in 2013, and Distinguished Alumnus Award from Indian Institute of Technology, Roorkee in 2014. He has also received ten best paper awards at international conferences.
Lost in Space: Design Manifolds can Accelerate Design and Optimization Iterations Several Fold
Dr. Mark Fuge
University of Maryland
November 13, 2020, 3:00 – 4:15 pm
Delivered via Zoom
https://osu.zoom.us/j/96782165738?pwd=V2d4Q3A2eTlBRE00SUwzLzNybWFOUT09
Meeting ID: 967 8216 5738
Password: 885842
ABSTRACT
When designing complex geometry like the surface of a turbine blade, engineers face a choice. They can use many surface control points (design variables) to achieve subtle changes that can lead to potentially important performance improvements — at the risk of themselves (or their optimizers) getting lost in the (often exponentially) larger design space that results. Or they can play it safe, using a lower-dimensional, standard design representation that they can tractably explore and optimize — at the risk of settling with lower performance designs. In this talk, I advocate for a different path; one that seemingly gets the best of both worlds. I propose learning a Design Manifold — a non-linear, low-dimensional subspace via Machine Learned Generative Models — that captures the key ways in which a design space varies by leveraging past examples of successful designs. I will describe this idea and then demonstrate how it aids gradient-free optimization via an example of airfoil design, where using Design Manifolds reduces the required design iteration time by 10x compared to traditional representations and 2-3x compared to State of the Art techniques. Importantly, these approaches do not require access to performance gradients (e.g., via adjoint solvers) and thus apply to any simulation code and assemblies with multiple parts.
About the Speaker:
Mark Fuge is an Assistant Professor of Mechanical Engineering at the University of Maryland, College Park, where he is also an affiliate faculty in the Institute for Systems Research and a member of the Maryland Robotics Center and Human-Computer Interaction Lab. His staff and students study fundamental scientific and mathematical questions behind how humans and computers can work together to design better complex engineered systems, from the molecular scale all the way to systems as large as aircraft and ships using tools from Computer Science (such as machine learning, artificial intelligence, complexity theory, and submodular optimization) and Applied Mathematics (such as graph theory, category theory, and statistics). He received his Ph.D. from UC Berkeley and has received an NSF CAREER Award, a DARPA Young Faculty Award, and a National Defense Science and Engineering Graduate (NDSEG) Fellowship. He gratefully acknowledges prior and current support from NSF, DARPA, ARPA-E, NIH, ONR, and Lockheed Martin, as well as the tireless efforts of his current and former graduate students and postdocs, upon whose coattails he has been graciously riding since 2015. You can learn more about his research at his lab’s website: http://ideal.umd.edu.
Accordions
Design and Manufacturing Seminar Series (Spring 2020)
Posted:
February 8, 2020
Manufacturing Simulation using Finite Element Technologies
Dr. Jeongho Kim
Director, Connecticut Manufacturing Simulation Center
Associate Professor, Department of Civil and Environmental Engineering,
Department of Mechanical Engineering,
University of Connecticut
March 27, 2020, 10:00 – 11:15 am
Scott Lab E525
ABSTRACT
Computer modeling and simulations can drastically lower the cost of creating a new manufacturing product by generating and evaluating a simulation in a fraction of the time it takes to build and test a prototype. Computer models and simulations using finite element technologies are important tools to optimize manufacturing process conditions which lead to less residual stress and distortions in a product. A variety of manufacturing processes can be simulated that include die quenching, cutting, spinning, sheet metal forming, hot press forming, welding, stamping, casting, injection modeling, and composites manufacturing. These processes require coupled thermo-mechanical analysis capabilities. In this seminar, an overview of the activities that Connecticut Manufacturing Simulation Center has worked with local small and medium-sized companies will be provided.
About the Speaker:
Prof. Jeongho Kim received a PhD from the University of Illinois at Urbana-Champaign in 2003, and has worked at the Department of Civil and Environmental Engineering in the University of Connecticut since 2004. Dr. Kim is the director of Connecticut Manufacturing Simulation Center. His area of expertise is in finite element methods, computational fracture mechanics, and composites damage modeling. Dr. Kim has led various research programs working with federal and state agencies and industry partners, including the National Institutes of Health, Pratt & Whitney, National Science Foundation, the Department of Homeland Security, the Air Force Research Laboratory, and the Connecticut Department of Transportation. Dr. Kim serves as an associate editor of Finite Element in Analysis and Design.
Data-Driven Design Analysis and Synthesis for Industrial Product Design
Dr. Levent Burak Kara
Professor of Mechanical Engineering & Robotics Institute
Carnegie Mellon University
April 10, 2020, 10:00 – 11:15 am
Scott Lab E525
ABSTRACT
Today’s design environments are equipped with powerful computational technologies more than ever before. However, as design software becomes increasingly more sophisticated, it also becomes more difficult to master, requiring an increased demand for human specialization and expertise. In the Visual Design and Engineering Lab at Carnegie Mellon, we are developing technologies that can leverage past designs as a way to assist today’s complex mechanical design workflows. Our fundamental strategy is to enable a level of design abstraction complemented with data, to appropriately support early ideation, conceptualization, reverse engineering, design optimization and physical fabrication.
In this talk, I will highlight our research in this area with an emphasis on how machine learning can aid in many of the conventionally tedious and expensive design steps. Examples will include robust sampling to reduce the cost in combinatorial design optimization scenarios, using past designs and concurrent shape analysis to extract domain specific design rules, crowdsourcing to learn semantic maps between human preferred design language and 3D computer models, and deep learned physics to replace expensive structural simulations. I will highlight the use of such approaches for both design analysis as well as generative design synthesis.
About the Speaker:
Levent Burak Kara is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University, with a courtesy appointment in the Robotics Institute. He is the founder of Visual Design and Engineering Laboratory. His research interests include computer-aided design, computer graphics, natural user interfaces
and machine learning, with applications in industrial product design, automotive design, engineering education and bio-medical engineering. He is the recipient of National Science Foundation CAREER award and American Society of Mechanical Engineers Design Automation Society Young Investigator Award. At CMU, he teaches courses in AI and Machine learning, Engineering Design, and Linear Algebra and Vector Calculus. He has a BS in Mechanical Engineering from the Middle East Technical University, and an MS and PhD in Mechanical Engineering from Carnegie Mellon University.
Accordions
Design & Manufacturing Seminar Series (Fall 2019)
Posted:
October 11, 2019
Topology optimization for additive manufacturing
Xiaoping Qian
Professor, Mechanical Engineering
University of Wisconsin-Madison
October 25, 2019, 10:00 – 11:15 am
Scott Lab E525
ABSTRACT
Additive manufacturing (AM) is capable of building parts of complex shapes that are difficult to produce with conventional manufacturing processes. Such shape flexibility opens up unprecedented design possibilities and has disruptive potentials for multiple technological fields. To unleash full potentials of the AM technology, computer methods that enable systematical exploration of such enlarged design space are thus needed.
In this talk, I will present our recent work in applying topology optimization to explore large design space enabled by AM. A density-gradient based formulation has been developed to account for AM constraints during topology optimization. It can control undercut and overhang angle in the optimized designs so they can be printed without support structures. It can also identify optimal build directions.
I will also present our recent work on topology optimization of conjugate heat transfer systems. Adjoint formulations for Navier-Stokes equations and convective heat transfer equations are derived to obtain the gradient for optimization. Numerical result shows that topologically optimized heat sinks significantly outperform conventional heat sinks in pressure drop and heat dissipation.
About the Speaker:
Xiaoping Qian is Professor in the Department of Mechanical Engineering at the University of Wisconsin-Madison. His research focuses on computational design of multi-physics systems, topology optimization, shape optimization, and design for additive manufacturing. His research has been supported by federal funding agencies such as AFOSR, ARO, ONR and NSF as well as private industries such as UTRC and GE. He has served as an associate editor for ASME Journal of Mechanical Design, Journal of Computing and Information Science in Engineering, Journal of Manufacturing Science and Engineering and journal Computer-Aided Design. He is an ASME fellow.
Data-Driven Design Analysis and Synthesis for Industrial Product Design
Levent Burak Kara
Professor of Mechanical Engineering & Robotics Institute
Carnegie Mellon Universit
November 1, 2019, 10:00 – 11:15 am
Scott Lab E525
ABSTRACT
Today’s design environments are equipped with powerful computational technologies more than ever before. However, as design software becomes increasingly more sophisticated, it also becomes more difficult to master, requiring an increased demand for human specialization and expertise. In the Visual Design and Engineering Lab at Carnegie Mellon, we are developing technologies that can leverage past designs as a way to assist today’s complex mechanical design workflows. Our fundamental strategy is to enable a level of design abstraction complemented with data, to appropriately support early ideation, conceptualization, reverse engineering, design optimization and physical fabrication.
In this talk, I will highlight our research in this area with an emphasis on how machine learning can aid in many of the conventionally tedious and expensive design steps. Examples will include robust sampling to reduce the cost in combinatorial design optimization scenarios, using past designs and concurrent shape analysis to extract domain specific design rules, crowdsourcing to learn semantic maps between human preferred design language and 3D computer models, and deep learned physics to replace expensive structural simulations. I will highlight the use of such approaches for both design analysis as well as generative design synthesis.
About the Speaker
Levent Burak Kara is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University, with a courtesy appointment in the Robotics Institute. He is the founder of Visual Design and Engineering Laboratory. His research interests include computer-aided design, computer graphics, natural user interfaces and machine learning, with applications in industrial product design, automotive design, engineering education and bio-medical engineering. He is the recipient of National Science Foundation CAREER award and American Society of Mechanical Engineers Design Automation Society Young Investigator Award. At CMU, he teaches courses in AI and Machine learning, Engineering Design, and Linear Algebra and Vector Calculus. He has a BS in Mechanical Engineering from the Middle East Technical University, and an MS and PhD in Mechanical Engineering from Carnegie Mellon University.
Advances and Challenges in Numerical Modeling of Impact Welding Process
Ali Nassiri
Research Assistant Professor, Integrated Systems Engineering
The Ohio State University
November 1, 2019, 10:00 – 11:15 am
Scott Lab E525
ABSTRACT
In impact welding, very high plastic strain regions develop; traditional FE analysis methods are not able to accurately simulate the process due to excessive element distortion near the contact region. Despite the great successes in developing hybrid and adaptive remeshing techniques, mesh-based numerical methods suffer from difficulties in some aspects which limit their applications in high-strain-rate problems. Recently, with the exponential growth in computer power, the next generation of computational methods, so called meshfree methods, have received significant attention. Among meshfree methods, smoothed particle hydrodynamics (SPH) has received major consideration. In this talk, the results from impact welding simulations between different metals including AM-Steel/AM-Steel, Copper/Copper, and Aluminum/Steel will be presented. Also, the future outlook to investigate the effect(s) of diffused coating on weldability using SPH platform will be discussed.
About the Speaker:
Ali Nassiri is a Research Assistant Professor at the Integrated Systems Engineering Department at the Ohio State University (OSU). He received his B.Sc. in Mechanical Engineering and M.Sc. in Aerospace Engineering from Sharif University of Technology, Tehran, Iran. After working in industry for a few years, he began his work at the University of New Hampshire (UNH) where he received his M.Sc. in Applied Mathematics and Ph.D. in Mechanical Engineering. He continued his career at UNH as a Postdoctoral Research Associate in the Mechanics, Materials & Manufacturing Lab until March 2016. In April 2016, he joined OSU as a Postdoctoral Researcher at the Center for Design and Manufacturing Excellence (CDME) with joint appointments in the Department of Materials Science and Engineering (MSE). He also served as a Research Scientist at the Simulation Innovation and Modeling Center (SIMCenter) for more than two years. Ali’s research interests include computational materials joining, non-conventional joining and forming processes, damage and failure analysis, materials characterization, dynamic behavior of materials, and mathematical modeling.