• Iterative Process Control for Laser Metal Deposition
  • GOALI: Volumetric Error Analysis of Machine Tools
  • Virtually Guided Certification of Computer Numerically Controlled Machine Tools
  • Integrated Part Variation Management
  • GAANN: Doctoral Research and Training in Mechatronics
  • Robotic Volumetric Error Compensation using Angular Measurements
  • REU Site: Additive Manufacturing
  • GOALI: Battery Health Dynamics and Its Management
  • Optimal Energy Scheduling in Microgrids

Iterative Process Control for Laser Metal Deposition


Iterative Process Control for Laser Metal Deposition

Dr. Robert G. Landers Project 1



INVESTIGATORS
Douglas Bristow (PI), Robert G. Landers, Frank Liou


FUNDING SOURCE
National Science Foundation
 

PROJECT DESCRIPTION
The research objective of this award is to develop robust, high-performance control algorithms for Laser Metal Deposition (LMD) processes. LMD is a class of additive manufacturing processes that builds metal parts layer by layer, introducing dynamics that propagate in time and also layer-domains. While conventional control strategies consider only the time-domain dynamics, the layer-domain dynamics introduce significant part defects that affect process reliability and efficiency. Physics-based 2-D (time- and layer-domain) dynamic models of the LMD process will be created and validated on a LMD machine at Missouri S&T and an industrial LMD machine at Optomec, Inc. New Repetitive Process Control (RPC) algorithms will be developed to control the 2-D LMD dynamics addressing issues of robustness and performance. The methodology employed may also be beneficial in other additive and layer-based manufacturing processes.
 

PUBLICATIONS

  1. “Frequency Domain Uncertainty Modeling and Quantification of the Laser Metal Deposition Process,” Sammons, P.M., Bristow, D.A., and Landers, R.G., 2016, Dynamic Systems and Control Conference, Minneapolis, Minnesota, October 12–14.
  2. A Model Predictive Repetitive Process Control Formulation for Additive Manufacturing Processes,” Sammons, P.M., Bristow, D.A., and Landers, R.G., 2015, Dynamic Systems and Control Conference, Columbus, Ohio, October 28–30.
  3. DC–Gain Layer–to–Layer Stability Criterion in Laser Metal Deposition Processes,” Sammons, P.M., Bristow, D.A., and Landers, R.G., 2015, Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin, Texas, August 10–12, pp. 1345–1355.
  4. Repetitive Process Control of Laser Metal Deposition,” Sammons, P.M., Bristow, D.A., and Landers, R.G., 2014, Dynamic Systems and Control Conference, San Antonio, Texas, October 22–24.
  5. “Frequency Domain Identification of a Repetitive Process Control Oriented Model for Laser Metal Deposition Processes,” Sammons, P.M., Bristow, D.A., and Landers, R.G., 2014, International Symposium on Flexible Automation, Hyogo, Japan, July 12–14 (Best Paper Finalist in Theory).
  6. Control–Oriented Modeling of Laser Metal Deposition as a Repetitive Process,” Sammons, P.M., Bristow, D.A., and Landers, R.G., 2014, American Control Conference, Portland, Oregon, June 4–6.
  7. Iterative Learning Control of Bead Morphology in Laser Metal Deposition Processes,” Sammons, P.M., Bristow, D.A., and Landers, R.G., 2013, American Control Conference, Washington DC, June 17–19.
  8. Height Dependent Laser Metal Deposition Process Modeling,” Sammons, P., Bristow, D.A., and Landers, R.G., 2013, ASME Journal of Manufacturing Science and Engineering, Vol. 135, No. 5, 054501 (7 pages)
  9. Height Dependent Laser Metal Deposition Process Modeling,” Sammons, P., Bristow, D.A., and Landers, R.G., 2012, International Symposium on Flexible Automation, St. Louis, Missouri, June 18–20.
  10. Layer–to–Layer Height Control for Laser Metal Deposition Processes,” Tang, L. and Landers, R.G., 2011, ASME Journal of Manufacturing Science and Engineering, Vol. 133, No. 2, 021009 (9 pages).
  11. Three Dimensional Layer Metal Deposition,” Ruan, J., Tang, L., Sparks, T.E., Liou, F.W., and Landers, R.G., 2010, ASME Journal of Manufacturing Science and Engineering, Vol. 132, No. 6, 064502 (6 pages).Tang, L. and Landers, R.G., 2010, “Melt Pool Temperature Control for Laser Metal Deposition Processes, Part I: Online Temperature Control,” ASME Journal of Manufacturing Science and Engineering, Vol. 132, No. 1, 011010 (9 pages).
  12. Melt Pool Temperature Control for Laser Metal Deposition Processes, Part II: Layer–to–Layer Temperature Control,” Tang, L. and Landers, R.G., 2010, ASME Journal of Manufacturing Science and Engineering, Vol. 132, No. 1, 011011 (9 pages).

GOALI: Volumetric Error Analysis of Machine Tools


GOALI: Volumetric Error Analysis of Machine Tools

Dr. Robert G. Landers Project 2


INVESTIGATORS
Robert G. Landers (PI) and Douglas Bristow
 

FUNDING SOURCE
National Science Foundation
 

PROJECT DESCRIPTION
The research objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) project is to create a new, viable paradigm for machine tool calibration and geometric error compensation. Traditional calibration techniques take dense, accurate error measurements using laser interferometers over a very narrow range of the machine tool operating space. In this work laser trackers, which have reduced accuracy compared to laser interferometers, are used to take error measurements over the entire machine tool operating space, fundamentally altering the calibration problem landscape and requiring new methods for synthesizing and identifying kinematic models. Position-dependent six degree-of-freedom kinematic errors will be introduced and identified at each joint, along with quantified levels of accuracy. The constructed kinematic models will provide a foundation for the creation of new compensation methodologies for table-based and real-time systems. The new machine tool calibration and compensation tools will be combined into a holistic volumetric error analysis framework that will provide users with predictive tools capable of quantifying the tradeoffs involved when calibrating and compensating a machine tool.
 

PUBLICATIONS 

  1. Table–Based Volumetric Error Compensation of Large 5–Axis Machine Tools,” Creamer, J., Sammons, P.M., Bristow, D.A., and Landers, R.G., 2017, ASME Journal of Manufacturing Science and Engineering, Vol. 139, No. 1, 021011 (11 pages).
  2. Modeling and Compensation of Joint–Dependent Kinematic Errors in Robotic Manipulators,” Ma, L., Bazzoli, P., Sammons, P.M., Bristow, D.A., and Landers, R.G., 2016, International Symposium on Flexible Automation, Cleveland, Ohio, August 1–3.
  3. “Population of Compensation Tables for 5–Axis Machine Tools Using Genetic Algorithms,” Creamer, J., Bristow, D.A., and Landers, R.G., 2014, International Symposium on Flexible Automation, Hyogo, Japan, July 12–14.
  4. Modeling and Compensation of Backlash and Harmonic Drive–Induced Errors in Robotic Manipulators,” Ma, L., Sammons, P.M., Embry, K., Armstrong, L., Bristow, D.A., and Landers, R.G., 2014, International Manufacturing Science and Engineering Conference, Detroit, Michigan, June 9–13.
  5. Table-Based Compensation for 5-Axis Machine Tools,” Creamer, J.R., Sammons, P.M., Bristow, D., Landers, R.G., Freeman, P., and Easley, S., 2013, ASME International Mechanical Engineering Congress and Exhibition, San Diego, California, November 13–21.

Virtually Guided Certification of Computer Numerically Controlled Machine Tools


Virtually Guided Certification of Computer Numerically Controlled 
Machine Tools via Digital Twin


INVESTIGATORS
Douglas Bristow (PI) and Robert G. Landers
 

FUNDING SOURCE
Digital Manufacturing Design Innovation Institute
 

PROJECT DESCRIPTION
This project envisions an innovative digital certification of the machining process that will reduce cost and mitigate startup delays. The envisioned technology will replace the experiential element in supplier pool selection with a digitally certified supplier pool that is based on quantitative technical data directly acquired from machining equipment in the supply chain. The goal of this project “Virtually Guided Certification of Computer Numerically Controlled Machine Tools via Virtual Twin” is to demonstrate virtual certification of machines tools at suppliers for successful manufacture of specific parts to significantly reduce the time and cost of first article certification and cost avoidance due to placement of parts at suppliers who cannot produce them on their machine tools. DMDII provides a unique opportunity for Boeing and Missouri S&T to leverage our prior work in machine tool calibration to develop tools that can virtually certify manufacturing equipment capabilities for a specific digital part via a machine digital twin. The developed tools will be realized in software application that will be owned by DMDII and available to DMDII industry members. The benefits for DMDII industry members are two-fold. For DMDII OEMs like Boeing, the software can be used to virtually certify parts prior to placing the work at that supplier. For instance, small parts may be suitable for many discrete suppliers while very large part may only be suitable for a handful of suppliers due to the capability of their manufacturing equipment. By virtually certifying the risk of each supplier to the specific parts being made, the request for quotes can be targeted to suppliers which can fabricate a successful part. Secondly, DMDII industry members acting as suppliers will be better able to make sound decisions in equipment acquisition and processes improvement by digitally certifying new equipment for specific parts. This will allow each supplier to quantitatively determine if their current capital equipment is suitable for the strategic business plan in part manufacture.

Integrated Part Variation Management


Integrated Part Variation Management


INVESTIGATORS
Douglas Bristow (PI), Robert G. Landers, Ming Leu


FUNDING SOURCE
Digital Manufacturing Design Innovation Institute


PROJECT DESCRIPTION
Incoming stock from casting and forging suppliers can vary to the point that standard machine tools cannot adequately respond to the existing material condition in the as-programmed state. Due to the machine tool’s inability to dynamically respond to material stock variation, issues such as broken tooling, scrap parts, and severe delays in the entire value stream often occur. The manufacturing community has attempted to solve this problem through part probing in the machine tool, programming sub-routines in the controller, and manual adjustments made by the machinist. The current approach yields sub-optimal processes that require significant human intervention and do not guarantee a conforming part. This project, which is a collaboration between Caterpillar, Missouri S&T, and the University of Illinois, aims to generate a system by which a manufacturer, in an automated fashion, can compensate for machine tool workspace errors induced due to part, fixture, tooling, or machine tool. Ideally, the system will be able to take actual part, tool, and fixture dimensional data, along with an error mapping of the machine tool, and generate 3-space transforms to compensate for the errors directly in the code output from the CAM package. This should allow for large reductions in setup times for new parts, new fixtures, or parts that see a large variation in the rough condition as delivered to the machining operation while minimizing human interaction in the machining setup process.

GAANN: Doctoral Research and Training in Mechatronics


GAANN: Doctoral Research and Training in Mechatronics


INVESTIGATORS
Douglas Bristow (PI), Kyle DeMars, Lian Duan, Edward Kinzel, Robert G. Landers, Ming Leu, Heng Pan, Jonghyun Park, and Hank Pernicka

 
FUNDING SOURCE
Department of Education


PROJECT DESCRIPTION
This project is a Graduate Assistance in Areas of National Need (GAANN) Fellowship program that will sustain and enhance the capacity for teaching and research in the area of mechatronics in the Mechanical and Aerospace Engineering (MAE) Department at the Missouri University of Science and Technology (Missouri S&T), formerly the University of Missouri–Rolla. Mechatronics refers to the sensing, actuation, and computer control systems of a mechanical system. It comprises the intelligence and autonomy of modern robotic systems, advanced manufacturing, unmanned air vehicles (UAVs), spacecraft, and alternative/hybrid energy systems.  Indeed, much of the performance, efficiency, and functionality gains in mechanical and aerospace systems over the past 30 years can be attributed to the mechatronic evolution of purely mechanical systems. The trend is expected to continue into the foreseeable future with rapidly growing markets in robotics, manufacturing, aerospace, and alternative energy. The general educational goal of the GAANN project is to attract eight talented individuals, at least three of which is from a traditionally underrepresented background, to pursue Ph.D. studies in mechatronics. The general research goal is for the Fellows’ research to contribute to the advancement of mechatronics knowledge and technology.

 

Robotic Volumetric Error Compensation using Angular Measurements


Robotic Volumetric Error Compensation using Angular Measurements

Dr. Robert G. Landers Project 6


INVESTIGATORS
Robert G. Landers (PI) and Douglas Bristow
 

FUNDING SOURCE
Automated Precision, Inc.

PROJECT DESCRIPTION
Robotic manufacturing uses six degree-of-freedom (DoF) industrial robots for machining tasks such as drilling, routing, polishing and milling. Compared to machine tools robotic manufacturing is significantly more flexible. Robots are capable of orienting the tool in the complete three-DoF rotational space, permitting operation on parts from all sides, including underneath, without refixturing. Robots are also capable of reaching inside of tight spaces or around obstacles. The primary drawback of robotic manufacturing is the decreased accuracy compared to machine tools. However, like machine tools, accuracy of the robot is much worse than its repeatability (typically an order of magnitude or more), and so accuracy can be significantly improved through proper compensation. Thus, there is a critical need to develop a method of measuring and compensating for robotic error.

In previous CAMT projects, the PIs developed fast Volumetric Error Compensation (VEC) algorithms for industrial robots using measurements acquired by a laser tracker and active target. However, the active target only acquires the position of the end effector, and multiple setups are required to obtain orientation information, which significantly increase measurement time. Furthermore, each set up introduces additional error. Alternatively, the API Smart Tracking System (STS) is an active target that can be used for laser tracker measurements while also providing orientation data with each measurement. Thus, the STS has great potential to significantly improve measurement time and potentially increase accuracy in robot VEC. In order to achieve this result, existing algorithms must be extended to use position and orientation measurements. The proposed work consists of the following tasks: 1) extend VEC algorithms for calibrating robots using STS measurement data (position and orientation), 2) test algorithms on a robot at Missouri S&T and analyze performance accuracy versus active target measurement methods, and 3) test algorithms on a robot at API or collaborator location.

 

REU Site: Additive Manufacturing


REU Site: Additive Manufacturing


INVESTIGATORS
Robert G. Landers (PI), Douglas Bristow, Lianyi Chen, Greg Hilmas, Edward Kinzel, Ming Leu, Frank Liou, Joe Newkirk, Heng Pan, Jill Schmid


FUNDING SOURCE
National Science Foundation


PROJECT DESCRIPTION
This three-year program at Missouri University of Science and Technology (Missouri S&T) is focused in the area of Additive Manufacturing (AM). Each year ten undergraduate students will join the Missouri S&T team of faculty and graduate students to engage in exciting, interdisciplinary AM research. To meet the interdisciplinary nature of AM, the proposed program will be a joint effort of the Departments of Mechanical and Aerospace Engineering, Manufacturing Engineering, Material Science and Engineering, and Information Science and Technology, as well as the Center for Aerospace Manufacturing Technologies on the Missouri S&T campus. The goals of this REU Site program are to: 1) educate undergraduate students to the awesome possibilities AM allows and stimulate them to learn more, 2) provide students with individual challenging research projects in AM that include analytical, computer aided design and analysis, and hands-on components in world class facilities with state-of-the-art equipment, 3) attract talented undergraduate students, particularly underrepresented students (at least 35% female and 20% minority) and students from institutions with limited STEM research opportunities (at least 50%), to conduct research in AM and motivate them to pursue graduate studies, and 4) provide activities that expose the students to other exciting areas of research and cutting-edge technologies, improve their communication skills, etc.
 

PUBLICATIONS 

  1. “Embedding of Optical Fiber in Glass for the Potential Production of Integrated Photonic Circuits,” Morrow, B., 2015, Poster presented at Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin, Texas, August 10–12.
  2. “Thermal History Modeling of Laser Metal Deposition Additive Manufacturing,” Malta, D., 2015, Poster presented at Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin, Texas, August 10–12.
  3. “How Substrate Properties Affect the Deposition Characteristics of Aerosol Jet Printed Features,” Agee, E., 2015, Poster presented at Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin, Texas, August 10–12.
  4. “An Easy Releasable, Perforated Elastomer Membrane for Epidermal Electronic Devices,” Denis, H., 2015, Poster presented at Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin, Texas, August 10–12.
  5. “Temperature Modeling of the Laser Metal Deposition Process,” Jimenez, J., 2015, Poster presented at Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin, Texas, August 10–12.
  6. “3D Extrusion Freeforming of Al2O3 using a CubePro© Printer,” Lapeyre, J., 2015, Poster presented at Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin, Texas, August 10–12.
  7. “Automating the Oil Filling and Infrared Heating Subsystem,” White, L., 2015, Poster presented at Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin, Texas, August 10–12.
  8. A Case Study of Topology Optimization for Additive Manufacturing,” Henley, L., 2015, Poster presented at Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin, Texas, August 10–12.
  9. “Development of an Ink for Printing Transient Electronic Devices,”  Green, T., 2015, Poster presented at Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin,  Texas, August 10–12.
  10. Solid Freeform Fabrication of Transparent Quartz Glass using a Filament Fed Process,” Luo, J., Gilbert, L.J., Qu, C., Morrow, B., Bristow, D.A., Landers, R.G., Goldstein, J., Urbas, A., and Kinzel, E.C., 2015, Twenty Sixth Annual Solid Freeform Fabrication Symposium, Austin, Texas, August 10–12, pp. 122–133.
  11. A Longitudinal Study on the Effectiveness of the Research Experience for Undergraduates (REU) Program at Missouri University of Science and Technology,” Liu, F., Nguyen, T.L, Sheng, H., and Landers, R.G., 2014, ASEE Annual Conference and Exposition, Indianapolis, Indiana, June 14–18.

 

GOALI: Battery Health Dynamics and Its Management


GOALI: Battery Health Dynamics and Its Management


Dr. Robert G. Landers Project 8


INVESTIGATORS
Jonghyun Park (PI) and Robert G. Landers

FUNDING SOURCE
National Science Foundation
 

PROJECT DESCRIPTION
The research project, which is a collaboration between Missouri S&T and EaglePicher, addresses unsolved questions essential to optimum battery management for lithium-ion batteries; how mechanical failures in battery materials affect chemical degradation, and eventually battery performance and capacity fade. Integration of high fidelity degradation mechanisms into the electrolyte-particle model will be used for the control of battery systems to maximize their performance and lifetime. This research aims at: (1) gaining a fundamental understanding of mechanical and chemical degradation mechanisms and incorporating this knowledge into a complete battery electrochemical model, (2) constructing a control-oriented model that quantitatively predicts capacity fade due to mechanical and chemical degradation mechanisms, as well as their coupled effects, and (3) leveraging this dynamic model to estimate battery state-of-health during operation based on limited signals, and utilize these estimates to optimize battery management system functions such as charging and cell balancing. If successfully realized, the solution for the fundamental challenges in energy storage systems, i.e., understanding the linkages between microscopic and macroscopic material behavior, and between failure and its control, will be realized. Further, the quantitative information regarding battery material failure on the microscopic level will be of particular use to the computational and modeling community.
 

PUBLICATIONS 

  1. Reduced–Order Electrochemical Model-Based SOC Observer with Output Model Uncertainty Estimation,” Lotfi, N., Landers, R.G., Li, J., and Park, J., 2016, IEEE Transactions on Control System Technology (to appear).
  2. Electrochemical Model–Based Adaptive Estimation of Li–Ion Battery State of Charge,” Lotfi, N., Landers, R.G., Li, J., and Park, J., 2015, Dynamic Systems and Control Conference, Columbus, Ohio, October 28–30.
  3. Development of an Experimental Testbed for Research in Li–Ion Battery Management Systems,” Lotfi, N., Fajri, P., Novosad, S., Savage, J., Landers, R.G., and Ferdowsi, M., 2013, Energies, Vol. 6, pp. 5231–5258.
  4. Robust Nonlinear Observer for State of Charge Estimation of Li–ion Batteries,” Lotfi, N. and Landers, R.G., 2012, ASME Dynamic Systems and Controls Conference, Fort Lauderdale, Florida, October 17–19 (best paper in session).
  5. Control-Oriented Thermal Modeling of Lithium-Ion Batteries from a First Principle Model via Model Reduction by the Global Arnoldi Algorithm,” Brown, D. and Landers, R.G., 2012, Journal of the Electrochemical Society, Vol. 159, No. 12, pp. A2043–A2052.

 

Optimal Energy Scheduling in Microgrids


Optimal Energy Scheduling in Microgrids with Photovoltaic Generation and Energy Storage Systems

INVESTIGATORS
Jonghyun Park (PI), Jonathan Kimball, and Robert G. Landers

FUNDING SOURCE
National Science Foundation


PROJECT DESCRIPTION
The project address a fundamental trade-off in the energy storage systems for power grid applications, which involves integration of PV generation requiring more frequent charge/discharge cycles and the corresponding degradation process. The research will find answers to the following unsolved questions that are essential for optimal energy scheduling of a microgrid: (1) how multiple degradation mechanisms of energy storage systems are coupled together, and how battery capacity fade is eventually affected by loading and environmental conditions; (2) what is the impact of uncertainty of solar radiation, market price, and load on battery loading profile, and how can we address uncertainty in optimal scheduling of a microgrid. Specifically, four objectives will be achieved: (1) gain fundamental understanding of battery degradation mechanisms and how they couple together, and integrate that knowledge into a high-fidelity battery model; (2) create a low-order battery model that can generate the predicted results quickly without losing accuracy; (3) understand the uncertainty phenomena in a microgrid, and create a model to incorporate all of the phenomena; and (4) develop algorithms for optimal scheduling by considering battery degradation and microgrid uncertainty. The proposed research will discover how the uncertain nature of a microgrid can affect individual components, including energy storage degradation, which will be a critical knowledge for the power engineering community. In addition, this project will develop a stochastic optimization technique for near-real-time optimal scheduling, so that applications can extend beyond power engineering.