Award Abstract # 1825133
Boosting the Speed and Accuracy of Vibration-Prone Manufacturing Machines at Low Cost through Software

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: REGENTS OF THE UNIVERSITY OF MICHIGAN
Initial Amendment Date: July 31, 2018
Latest Amendment Date: July 31, 2018
Award Number: 1825133
Award Instrument: Standard Grant
Program Manager: Khershed Cooper
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: August 15, 2018
End Date: July 31, 2021 (Estimated)
Total Intended Award Amount: $337,575.00
Total Awarded Amount to Date: $337,575.00
Funds Obligated to Date: FY 2018 = $337,575.00
History of Investigator:
  • Chinedum Okwudire (Principal Investigator)
    okwudire@umich.edu
Recipient Sponsored Research Office: Regents of the University of Michigan - Ann Arbor
1109 GEDDES AVE STE 3300
ANN ARBOR
MI  US  48109-1015
(734)763-6438
Sponsor Congressional District: 06
Primary Place of Performance: Regents of the University of Michigan
3003 South State St.
Ann Arbor
MI  US  48109-1274
Primary Place of Performance
Congressional District:
06
Unique Entity Identifier (UEI): GNJ7BBP73WE9
Parent UEI:
NSF Program(s): Manufacturing Machines & Equip
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 082E, 083E, 9102
Program Element Code(s): 146800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

Quality, productivity and cost are three key pillars of manufacturing. To stay competitive in an increasingly global economy, U.S. manufacturers must find ways of improving the quality and productivity of their manufacturing processes while keeping costs low. Most manufacturing machines tend to vibrate as they move, due to weaknesses in their mechanical structures. The resultant motion-induced vibration adversely affects the accuracy and speed of the manufacturing machines, thus degrading the quality and productivity of the associated manufacturing processes. Software solutions that involve generating motion commands to avoid unwanted vibration of the machines are very attractive in practice because they are low cost and, unlike hardware solutions, they do not add to machine weight and size. However, existing software solutions sacrifice motion speed and/or accuracy, or are impractical because they cannot properly handle uncertainties and variabilities that occur during normal usage of the machines. This award supports a scientific investigation into a software-based vibration mitigation approach that shows great promise to overcome the technical and practical shortcomings of existing software solutions. The approach involves representing the desired machine tools in B-splines, then modifying them to account for characteristics of the machine. To keep calculations manageable, tool motions will not be calculated for the entire part but will be calculated for a "window" around the current tool location. Knowledge created through this scientific investigation will enable industry to boost the accuracy and speed of manufacturing machines at low cost, thus increasing their competitiveness in the global marketplace. This directly affects a number of economic sectors, including medical devices, automotive, aerospace and defense; it therefore directly and positively impacts both economic competitiveness and national security. The broader impact plan includes: educating students and industry about software based vibration mitigation methods through curriculum development and (online) tutorials; and K-12 outreach to motivate underrepresented minority students to STEM fields by demonstrating the benefits of software-based vibration mitigation techniques on desktop 3D printers.


The objective of the work is to mathematically characterize and experimentally validate the effects of limited-preview filtering of B-splines on the accuracy and speed of manufacturing machines that suffer from motion-command-induced vibration. The motion commands for a vibration-prone machine will be represented as B-splines. To facilitate computationally efficient online vibration compensation, the B-splines will be filtered in small batches (limited preview) using a model of machine dynamics. However, limited-preview filtering of B-splines introduces approximation errors with poorly understood effects on the accuracy and versatility of online vibration compensation. Methods from linear systems theory will be employed to characterize and mitigate the effects of the approximation errors. Moreover, effects of uncertainties in machine dynamics on the accuracy of filtered B-splines will be analyzed mathematically with a goal of maximizing the robustness of online vibration compensation to variations in system dynamics. Lastly, techniques from model predictive control will be leveraged to develop a scientific methodology for maximizing the speed of vibration-prone machines without sacrificing positioning accuracy. The theoretical understanding and methods developed through this research will be validated experimentally on 3D printers and various other vibration-prone manufacturing machines.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Chou, Cheng-Hao and Duan, Molong and Okwudire, Chinedum E. "A linear hybrid model for enhanced servo error pre-compensation of feed drives with unmodeled nonlinear dynamics" CIRP annals , v.70 , 2021 https://doi.org/https://doi.org/10.1016/j.cirp.2021.04.070 Citation Details
Edoimioya, Nosakhare and Ramani, Keval S. and Okwudire, Chinedum E. "Software compensation of undesirable racking motion of H-frame 3D printers using filtered B-splines" Additive Manufacturing , v.47 , 2021 https://doi.org/10.1016/j.addma.2021.102290 Citation Details
Kim, Heejin and Okwudire, Chinedum E. "Accurate and computationally efficient approach for simultaneous feedrate optimization and servo error pre-compensation of long toolpathswith application to a 3D printer" The International Journal of Advanced Manufacturing Technology , v.115 , 2021 https://doi.org/10.1007/s00170-021-07200-5 Citation Details
Kim, Heejin and Okwudire, Chinedum E. "Simultaneous servo error pre-compensation and feedrate optimization with tolerance constraints using linear programming" The International Journal of Advanced Manufacturing Technology , v.109 , 2020 10.1007/s00170-020-05651-w Citation Details
Ramani, Keval S. and Duan, Molong and Okwudire, Chinedum E. and Ulsoy, A. Galip "Optimal Selection of Basis Functions for Minimum-Effort Tracking Control of Nonminimum Phase Systems Using Filtered Basis Functions" Journal of Dynamic Systems, Measurement, and Control , v.141 , 2019 10.1115/1.4044355 Citation Details
Ramani, Keval S. and Edoimioya, Nosakhare and Okwudire, Chinedum E. "A Robust Filtered Basis Functions Approach for Feedforward Tracking ControlWith Application to a Vibration-Prone 3-D Printer" IEEE/ASME Transactions on Mechatronics , v.25 , 2020 https://doi.org/10.1109/TMECH.2020.2983680 Citation Details
Ramani, Keval S. and Okwudire, Chinedum E. "Optimal Selection of Basis Functions for Robust Tracking Control of Linear Systems using Filtered Basis Functions" 2020 American Control Conference (ACC) , 2020 10.23919/ACC45564.2020.9147557 Citation Details
Ramani, Keval S. and Okwudire, Chinedum E. "Optimal Selection of Basis Functions for Robust Tracking Control of Uncertain Linear SystemsWith Application to Three-Dimensional Printing" Journal of Dynamic Systems, Measurement, and Control , v.143 , 2021 https://doi.org/10.1115/1.4051097 Citation Details
Ramani, Keval S. and Okwudire, Chinedum E. "Robust Filtered Basis Functions Approach for Feedforward Tracking Control" ASME 2018 Dynamic Systems and Control Conference , 2018 10.1115/DSCC2018-9196 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

Quality, productivity and cost are three key pillars of manufacturing. To stay competitive in an increasingly global economy, U.S. manufacturers must find ways to improve the quality and productivity of their manufacturing processes while keeping costs low. Manufacturing machines tend to vibrate as they move, due to weaknesses in their mechanical structures. The resultant motion-induced vibration adversely affects the accuracy and speed of the machines, thus degrading the quality and productivity of the associated manufacturing processes. Software solutions that involve generating motion commands to avoid unwanted vibration of the machines are very attractive in practice because they are low cost and, unlike hardware solutions, they do not add to machine weight and size. However, existing software solutions sacrifice motion speed and/or accuracy, or are impractical because they cannot properly handle uncertainties and variabilities that occur during normal usage of the machines. This award has supported a scientific investigation into a software-based vibration mitigation approach that shows great promise to overcome the technical and practical shortcomings of existing software solutions.

Intellectual Merit

The objective of the proposed work was to mathematically characterize and experimentally validate the effects of the limited-preview filtered B-splines (LPFBS) method on the accuracy and speed of manufacturing machines that suffer from motion-command-induced vibration. Three tasks were proposed as part of its intellectual merit:

Task 1: Characterization and Improvement of Accuracy and Versatility of the LPFBS Method

Task 2: Characterization and Improvement of Robustness of the LPFBS Method

Task 3: Maximization of Motion Speed using the LPFBS Method

As part of Task 1, an optimal version of the LPFBS algorithm was developed. The new algorithm was analyzed and found to be much more efficient in terms of control effort compared to the LPFBS algorithm. It was shown to reduce the error in tracking the motion of a vibration-prone precision motion stage by up to 19 times.

As part of Task 2, two robust algorithms were developed to deal with uncertainty in manufacturing machines. The first algorithm used B-splines designed to improve robustness; it was shown in experiments to reduce errors in tracking the motion of a vibration prone 3D printer by up to 16%. The second algorithm optimized the basis functions for robustness. The paper that proposed the first algorithm was selected as one of six finalists from a pool of 284 papers for the IEEE/ASME Transactions in Mechatronics Best Paper Award (http://www.ieee-asme-mechatronics.info/2021-results/).

As part of Task 3, a linear programming approach was developed to optimize motion speed by leveraging the LPFBS vibration compensation algorithm. The approach was shown in experiments to yield up to 25% reduction in cycle time without sacrificing printing quality relative to the conventional approach of optimizing motion speed without vibration compensation included.

Broader Impacts

A bottom-up approach was proposed to help translate the LPFBS method to industry. It involved first bringing the algorithm to low-cost desktop 3D printers and then leveraging the results from desktop 3D printers to translate it to higher-end machines. In this vein, the PI founded a start-up company (Ulendo, www.ulendo.io) to help translate the LPFBS algorithm to industry. The company has recently received an STTR grant from the NSF. It has also received a seed round of funding from venture capitalists and is now in the process of signing contracts to integrate the LPFBS algorithm on the 3D printers of two major 3D printer manufacturers.

Educational curricula that teach students and practicing engineers about software-based vibration compensation methods have been developed. Specifically, a software-based vibration compensation tutorial was developed and presented two times at the annual meetings of the American Society for Precision Engineering. It received excellent reviews and in both cases were attended by participants from industry and academia. The LPFBS algorithm has also been introduced into a graduate course taught at the University of Michigan.

The projects has trained four PhD students - including an underrepresented minority student and a female student. All four students are interested in faculty positions. One has already become an assistant professor at a prestigious university. The training of industry members through the developed tutorial has equipped practicing engineers with knowledge about software-based vibration compensation.

The results of this project have been disseminated in manufacturing and controls conferences. They have also been published in very reputable journals in manufacturing, controls, and mechatronics. A total of 7 journal papers and 10 conference papers have resulted from this project.


Last Modified: 10/24/2021
Modified by: Chinedum Okwudire

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