
NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
Recipient: |
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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: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1109 GEDDES AVE STE 3300 ANN ARBOR MI US 48109-1015 (734)763-6438 |
Sponsor Congressional District: |
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Primary Place of Performance: |
3003 South State St. Ann Arbor MI US 48109-1274 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Manufacturing Machines & Equip |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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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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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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