Award Abstract # 1932264
CPS: Medium: Collaborative Research: Frequency Domain Conversion of Computer Aided Design Files to Enable Encryption, Authentication and Feature Search Function

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: NEW YORK UNIVERSITY
Initial Amendment Date: August 22, 2019
Latest Amendment Date: May 19, 2021
Award Number: 1932264
Award Instrument: Standard Grant
Program Manager: Bruce Kramer
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: September 1, 2019
End Date: June 30, 2022 (Estimated)
Total Intended Award Amount: $242,697.00
Total Awarded Amount to Date: $274,697.00
Funds Obligated to Date: FY 2019 = $242,697.00
FY 2020 = $16,000.00

FY 2021 = $16,000.00
History of Investigator:
  • Nikhil Gupta (Principal Investigator)
    ngupta@nyu.edu
  • Ramesh Karri (Co-Principal Investigator)
Recipient Sponsored Research Office: New York University
70 WASHINGTON SQ S
NEW YORK
NY  US  10012-1019
(212)998-2121
Sponsor Congressional District: 10
Primary Place of Performance: New York University
NY  US  10012-1019
Primary Place of Performance
Congressional District:
10
Unique Entity Identifier (UEI): NX9PXMKW5KW8
Parent UEI:
NSF Program(s): AM-Advanced Manufacturing,
CPS-Cyber-Physical Systems
Primary Program Source: 01001920DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT

01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 016Z, 067E, 116E, 152E, 7924, 9178, 9231, 9251, MANU
Program Element Code(s): 088Y00, 791800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

The additive manufacturing (AM) or 3D printing market has developed exponentially from $1.1B in 2009 to $7.3B in 2018 and experts estimate this to grow double in the next four years. Despite the strategic role that computer-aided design (CAD) files play in AM, designers develop, store, and use the CAD models as they do in any other manufacturing method. This study will research a novel method of imparting new functionalities in CAD models by converting them to the frequency domain using lossless algorithms. The frequency domain representation allows searching the files for specific design features of interest, which is not feasible in the current CAD file formats. Transformation to the frequency domain opens up possibilities for developing new compression and encryption methods.

Analysis will be conducted to determine the fundamental principles that lead to lossless conversion of CAD files to the frequency domain and develop compression and encryption methods for the files. Wavelet transform and dynamic time warping will be among the methods used for implementing search functionalities. Dynamic window selection and scaling methods will be used to perform feature search with and without scaling of the object size. These algorithms are based on an exact match procedure and do not rely on tuning or learning the algorithm. The project will help industries that are using AM methods, for example, the aerospace, automotive, medical and military equipment industries. Development of these unique capabilities will by provide new capabilities for design, search, and security. The project will involve post-doctoral fellows and graduate students in the research. The dissemination plan will include publishing the results in journals, conference presentations and press releases for public awareness using the news media.

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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Gupta, Nikhil and Tiwari, Akash and Bukkapatnam, Satish T. S. and Karri, Ramesh "Additive Manufacturing Cyber-Physical System: Supply Chain Cybersecurity and Risks" IEEE Access , v.8 , 2020 https://doi.org/10.1109/ACCESS.2020.2978815 Citation Details
Li, Wenjin and Mac, Gary and Tsoutsos, Nektarios Georgios and Gupta, Nikhil and Karri, Ramesh "Computer aided design (CAD) model search and retrieval using frequency domain file conversion" Additive Manufacturing , v.36 , 2020 https://doi.org/10.1016/j.addma.2020.101554 Citation Details
Mac, Gary and Pearce, Hammond and Karri, Ramesh and Gupta, Nikhil "Uncertainty Quantification in Dimensions Dataset of Additive Manufactured NIST Standard Test Artifact" Data in Brief , 2021 https://doi.org/10.1016/j.dib.2021.107286 Citation Details
Mahesh, Priyanka and Tiwari, Akash and Jin, Chenglu and Kumar, Panganamala R. and Reddy, A. L. Narasimha and Bukkapatanam, Satish T. S. and Gupta, Nikhil and Karri, Ramesh "A Survey of Cybersecurity of Digital Manufacturing" Proceedings of the IEEE , v.109 , 2021 https://doi.org/10.1109/JPROC.2020.3032074 Citation Details
Mouris, Dimitris and Gouert, Charles and Gupta, Nikhil and Tsoutsos, Nektarios Georgios "Peak Your Frequency: Advanced Search of 3D CAD Files in the Fourier Domain" IEEE Access , v.8 , 2020 https://doi.org/10.1109/ACCESS.2020.3013284 Citation Details
Tsoutsos, Nektarios Georgios and Gupta, Nikhil and Karri, Ramesh "Cybersecurity Road Map for Digital Manufacturing" Computer , v.53 , 2020 https://doi.org/10.1109/MC.2020.3003432 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.

Computer aided design (CAD) models play an important role in numerous design and manufacturing industry sectors. Recent advances in CAD modeling, resulting in generating design methods, have allowed creating numerous variations of a part design based on the applied load constraints. Despite such advancements in the design capabilities, the CAD programs lack the capability of searching the design files based on the model geometry. The available search algorithms mostly work on using the metadata, which, sometimes, is inserted manually. Such manual tasks are not practical with generative design methods that can create thousands of geometric variations of a design in a short time frame. The objective of this project was to develop a search algorithm that works purely on the model geometry and does not make use of metadata. The preliminary results that showed that the CAD files can be converted to frequency domain using lossless transforms and the frequency domain spectrograms can be used for developing new search methods. The major goals of this project were: (a) Development of fundamental principles for converting CAD models to audio and then back to CAD model without any loss of information. Development of algorithms based on these principles and testing of their robustness for lossless conversion and (b) Development of search functionality and authentication methods in audio format and evaluation of robustness of these methods. The availability of CAD model search capability will help industries that are using design and additive manufacturing AM methods, for example, the aerospace, automotive, and consumer hardware parts industries. The work resulted in development of algorithms for several use cases, for example, conducting the search on models oriented on different axes, the same models built by different CAD workflows, and the same model geometries of different sizes. A similarity index was developed to numerically display the similarity of two models and conduct a broader search. A higher score indicated a better match. This scheme allowed showing many different models on the screen as potential matrches with various degrees of similarities for the user to pick additional options if the top score is not the one most appropriate. The work resulted in six journal articles, seven conference presentations and a web-based system for search of CAD models based on geometry. The web interface front and back end are designed for intuitive and efficient search function while each search saves the model in the database for continuous growth and sustainability.


Last Modified: 11/14/2022
Modified by: Nikhil Gupta

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