Prahalada Rao, PhD

Associate Professor

Academic Degrees

  • Ph.D., Industrial Engineering; Oklahoma State University (2013)
  • M.S., Industrial Engineering; Oklahoma State University (2006)
  • B.Eng. degree (First Class) Production Engineering, Victoria Jubilee Technical Institute (VJTI), Bombay University, India (2003)

Curriculum Vitae (CV):

Areas of Research and Professional Interest

Manufacturing|Sensing|Analytics

LAMPS: Laboratory for Advanced Manufacturing Process and Sensing

Click  here for the LAMPS lab website.

Click here for a longer and "easy reading" synopsis of my research.

  • Monitoring of ultraprecision nanomanufacturing and additive manufacturing (AM) processes: Empirical and phenomenological study of process machine interactions in ultraprecision machining (UPM), diamond turning (DT),  semiconductor chemical mechanical planarization (CMP), fused deposition modeling (FDM), and selective laser sintering (SLS).

  • Sensor-based predictive analytics and quality monitoring of data rich complex systems: Novel neuro-dynamic and graph theoretic image processing algorithms for real-time defect detection in manufacturing processes from heterogeneous sensor data.

  • Surface Morphology and dimensional integrity monitoring: Approaches for non-destructive quantification of ultraprecision surface morphology and dimensional integrity assurance of complex non-euclidean surfaces.

  • Design of wireless sensors networks and digital data acquisition: Customization of wired, as well as wireless sensors for high-speed data acquisition in manufacturing and healthcare applications.

Courses Taught

  • MECH 498/898 Additive Manufacturing
  • MECH 321 Statistics and Data Analysis
  • MECH 422/822 Industrial Quality Control

About Prahalada Rao

Prahalada Rao’s scholastic passion can be encapsulated in three words: Manufacturing, Sensing, and Analytics. His research focuses on thermal modeling, in-process sensor-based monitoring, and diagnosis of additive manufacturing processes (3D printing) . He is the recipient of multiple grants from the National Science Foundation (NSF), including the 2018 NSF CAREER award. He earned the 2017 Yoram Koren Outstanding Young Manufacturing Engineer Award by the Society of Manufacturing Engineers.  At UNL Dr. Rao teaches courses in Additive Manufacturing (MECH 498/898), Statistics (MECH 321), and Quality Control (MECH 422/822). He is an amateur radio ARRL General Class licensee with the call sign K5RAO. 

Experience

  • Assistant Professor (2016-present), Mechanical and Materials Engineering
    University of Nebraska-Lincoln
  • Assistant Professor (2014-2016), Systems Science and Industrial Engineering            
    Binghamton University (State University of New York)                                                  
  • Post-Doctoral Research Associate (2013-2014), Industrial and Systems Engineering
    Virginia Polytechnic Institute and State University (Virginia Tech)     
    Supervisor: Dr. Zhenyu (James) Kong

Honors and Awards

  • Outstanding Reviewer, Society of Manufacturing Engineers, Journal of Manufacturing Systems, 2018
  • Society of Manufacturing Engineers, Outstanding Young Manufacturing Engineer Award, 2017.
  • Finalist: IIE Manufacturing and Design Division Young Investigator Award, 2016
  • Nominated for Institute of Industrial Engineers, Pritsker Doctoral Dissertation Award, 2014
  • Nominated for university-wide Dissertation Award, 2013
  • Finalist, Institute of Industrial Engineers, John L. Imhoff graduate fellowship,2011
  • Outstanding Research Assistant Award, Alpha Pi Mu, Oklahoma State University chapter, 2008

Funding

  • National Science Foundation – CMMI 1752069, CAREER: Smart Additive Manufacturing: Fundamental Research in Sensing, Data Science, and Modeling Toward Zero Part Defects. 2018-2023
  • National Science Foundation – CMMI 1739696, CPS: Medium: Collaborative Research: Cyber-Enabled Online Quality Assurance for Scalable Additive Bio-Manufacturing. 2017-2021
  • National Science Foundation – CMMI 1719388, Biosensor Data Fusion for Real-time Monitoring of Global Neurophysiological Function.  2015 - 2018

Selected Publications

  1. Z. Smoqi, J. Toddy, H. Halliday, J. E. Shield, and P. Rao. Process-Structure Relationship in the Directed Energy Deposition of Cobalt-Chromium Alloy Coatings. Materials and Design, Volume 197, January 2021. doi: 10.1016/j.matdes.2020.109229

  2. S. Ramesh, Y. Zhang, D. Cormier, O. Harrysson, P. Rao, A. Tamayol, I. Rivero Extrusion Bioprinting: Recent Progress, Challenges, and Future Opportunities. Bioprinting, (In-Press) doi: 10.1016/j.bprint.2020.e00116

  3. H. Yang, P. Rao, T. Simpson, Y. Lu, P. Witherell, A. R. Nassar, E. Reutzel, and S. Kumara Six-sigma Quality Management of Additive Manufacturing. Proceedings of the IEEE (In-Press) doi: 10.1109/JPROC.2020.3034519

  4.  J. Liu, J. Zheng, P. Rao, and Z. KongMachine learning–driven in situ process monitoring with vibration frequency spectra for chemical mechanical planarization. International Journal Advanced Manufacturing Technology, 111, 1873–1888 (2020). https://doi.org/10.1007/s00170-020-06165-1

  5. A. C. Gaikwad, B. Giera, G.M. Guss, J-B Forien, M. J. Matthews, and P. Rao. Heterogeneous Sensing and Scientific Machine Learning for Quality Assurance in Laser Powder Bed Fusion – A Single-track Study. Additive Manufacturing (Accepted, In-Press, October 7th, 2020). doi:/10.1016/j.addma.2020.101659

  6.   R. Yavari, R.J. Williams, K. Cole, P. Hooper, and P. Rao. Thermal Modeling in Metal Additive Manufacturing using Graph Theory: Experimental Validation with In-situ Infrared Thermography Data from Laser Powder Bed Fusion. ASME Transactions, Journal of Manufacturing Science and Engineering, 142(12): 121005, 2020. doi: 10.1115/1.4047619.

  7. J. Williams, P. Rao, A. Samal, M. Johnson. Paired Trial Classification: A Novel Deep Learning Technique for MVPA. Frontiers of Neuroscience, Volume 14, Issue 47, April 2020. doi: 10.3389/fnins.2020.00417 

  8.  R. Salary, J.P. Lombardi, D. L. Weerawane, M.S. Tootooni, P. Rao, M. Poliks. A Sparse Representation-based Classification (SRC) Approach for Near Real-time Functional Monitoring of Aerosol Jet-Printed Electronic Devices. ASME Transactions, Journal of Manufacturing Science and Engineering 142(8): 081007, 2020.  doi:/10.1115/1.4047045

  9.  K. Cole, R. Yavari, and P.Rao. Computational heat transfer with spectral graph theory: Quantitative verification, International Journal of Thermal Sciences. Volume 153, July 2020. doi: 10.1016/j.ijthermalsci.2020.106383

  10. S. Gerdes, A. Mostafavi, S. Ramesh, A. Memic, I. Rivero, P. Rao, and A. Tamayol. Process-Structure-Quality Relationships of 3D Printed PCL-Hydroxyapatite Scaffolds, Tissue Engineering (Part A), (Accepted, in-press, available online). doi: 10.1089/ten.TEA.2019.0237

  11. A.C. Gaikwad, R. Yavari, M. Montazeri, K. Cole, L. Bian, P. Rao. Toward the Digital Twin in Metal Additive Manufacturing – Integrating Thermal Simulations, Sensing, and Analytics to Detect Process Faults, IISE Transactions (Accepted) doi: 10.1080/24725854.2019.1701753

  12. A.C. Gaikwad, F. Imani, H. Yang, E. Reutzel, and, P. Rao Prediction of Build Quality in Laser Powder Bed Fusion using Deep Learning of In-Situ Images, ASTM Journal of Smart and Sustainable Manufacturing System 3 (1), pp. 98-121, 2019. doi:10.1520/SSMS20190027

  13. M. Montazeri, A. Nassar. C. Stutzman, P. Rao Heterogeneous Sensor-based Condition Monitoring in Directed Energy Deposition, Additive Manufacturing, Volume 30, December 2019, 100916. doi.org/10.1016/j.addma.2019.100916.

  14. M. Amini, S.I. Chang, P. Rao. A Cybermanufacturing and Artificial Intelligence Framework for Laser Powder Bed Fusion (LPBF) Additive Manufacturing Process, Manufacturing Letters, 21, pp. 41-44, 2019. doi:10.1016/j.mfglet.2019.08.007

  15. M. Montazeri, A. Nassar, A. Dunbar, P. Rao, In-Process Monitoring of Porosity in Additive Manufacturing Using In-Process Optical Emission Spectroscopy Signals, IISE Transactions (Manufacturing and Design), 2019, Accepted, In-Press. doi: 0.1080/24725854.2019.1659525
  16. R. Yavari, K. Cole, P. Rao, Thermal Modeling in Metal Additive Manufacturing using Graph Theory. ASME Transactions, Journal of Manufacturing Science and Engineering, 2019, Vol. 141, pp. 0710071-20. doi: 10.1115/1.4043648

  17. M. Roy, R. Yavari, C. Zhou, O. Wodo, and P. Rao. Prediction and Experimental Validation of Part Thermal History in Fused Filament Fabrication Additive Manufacturing Process, ASME Transactions, Journal of Manufacturing Science and Engineering, 141(12), pp. 121001-10, 2019. doi: 10.1115/1.4045056

  18. J. Lombardi, R. Salary, D. Weerawarne, P.Rao, M. Poliks,  Image-Based Closed-Loop Control of Aerosol Jet Printing Using Classical Control Methods, ASME Transactions, Journal of Manufacturing Science and Engineering, 141(7), 071011-20, 2019. doi: 10.1115/1.4043659

  19. L.J. Rhodes, M. Rios, J. Williams, G. Quiñones, P. Rao, V. Miskovic, The Role of Low-Level Image Features in The Affective Categorization Of Rapidly Presented Scenes, PLoS ONE 14(5): e0215975. doi: 10.1371/journal.pone.0215975

  20. F. Imani, B. Yao, R. Chen, P. Rao, H. Yang, Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control (Technical Brief), ASME Transactions, Journal of Manufacturing Science and Engineering, 141(4), 044501-08, 2018. doi: 10.1115/1.4042579.

  21. J. Williams, P. Dryburgh, A. Clare, P. Rao, A. Samal, Defect Detection and Monitoring in Metal Additive Manufactured Parts through Deep Learning of Spatially Resolved Acoustic Spectroscopy Signals. ASTM Journal of Smart and Sustainable Manufacturing, Vol. 2(1), 204-226, 2018. doi/10.1520/SSMS20180035

  22. J. Liu, C. Liu, Y. Bai, Z. Kong, P. Rao, and C. Williams. Layer-wise Spatial Modeling of Porosity in Additive Manufacturing. IISE Transactions, (Additive Manufacturing Special Issue), Accepted, In-Press, 2018. Article Highlighted in January 2019 issue of the Industrial and Systems Engineer Magazine. doi:/10.1080/24725854.2018.1478169

  23. F. Imani, A. Gaikwad, M. Montazeri, P. Rao, H. Yang, E. Reutzel. Process Mapping and In-Process Monitoring of Porosity in Laser Powder Bed Fusion Using Layerwise Optical Imaging. ASME Transactions, Journal of Manufacturing Science and Engineering, 140(10), 101009-23, 2018. doi: 10.1115/1.4040615

  24. X. Wang, M. Sealy, R. Williams, P. Rao, Y. Guo. Stochastic Modeling and Analysis of Spindle Energy Consumption During Hard Milling. ASME Transactions, Journal of Manufacturing Science and Engineering, 140(6), 060801-14, 2018. doi: 10.1115/1.4038644

  25. M. Montazeri, P. Rao. Heterogeneous Sensor-based Build Condition Monitoring in Laser Powder Bed Fusion Additive Manufacturing Process using a Spectral Graph Theoretic Approach. ASME Transactions, Journal of Manufacturing Science and Engineering, 140(9), 091002-18, 2018. doi: 10.1115/1.4040264

  26. M. Montazeri, R. Yavari, P. Rao, P. Boulware. In-process Monitoring of Material Cross-Contamination Defects in Laser Powder Bed Fusion. ASME Transactions, Journal of Manufacturing Science and Engineering, 140(11), 111001-20, 2018. doi: 10.1115/1.4040543

  27. M. Sealy, G. Madireddy, R. Williams, P. Rao, M. Toursangsaraki. Review Article - Hybrid Processes in Additive Manufacturing. ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 140(6), pp. 060801-14, 2018. doi:10.1115/1.4038644.

  28. H. Sun, P. Rao, Z. Kong, X. Deng and R. Jin. Functional Quantitative and Qualitative Models for Quality Modeling in a Fused Deposition Modeling Process. IEEE Transactions, Automation Science and Engineering, Vol. 15(1), pp. 393-403, 2018. doi: 10.1109/TASE.2017.2763609.

  29. M. S. Tootooni, P. Rao, C-A. Chou, Z. Kong.  A Spectral Graph Theoretic Approach for Monitoring Multivariate Time Series Data from Complex Dynamical Processes. IEEE Transactions, Automation Science and Engineering, Vol.15(1), pp.127-144, 2018. doi: 10.1109/TASE.2016.2598094

  30. M. Khanzadeh, P. Rao, R. Jafari-Marandi, B. K. Smith, M. Tschopp, L. Bian. Characterizing the Geometric Accuracy of Additively Manufactured Components Using Self-Organizing Maps. ASME Transactions, Journal of Manufacturing Science and Engineering, Vol 140(3), pp. 031011-  031023, 2017. doi: 10.1115/1.4038598

  31. M. Aboutaleb, M. Tschopp, P. Rao, L. Bian. Accelerated Multiobjective Optimization of Part Geometric Accuracy in Additive Manufacturing (AM). ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 139(10), pp. 101001 – 101014, 2017. doi: 10.1115/1.4037319

  32. R. Salary, J. Lombardi, P. Rao, M. Poliks. Aerosol Jet Printing (AJP) of Flexible Electronic Devices: Online Monitoring of Functional Electrical Properties Using Shape-from-Shading (SfS) Image Analysis. ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 139(10), pp. 101010 – 101023, 2017. doi:10.1115/1.4036660

  33. M.S. Tootooni, A. Dsouza, R. Donovan, P. Rao, Z. Kong, P. Borgesen. Classifying the Dimensional Variation in Additive Manufactured Parts from Laser-Scanned 3D Point Cloud Data using Machine Learning Approaches. ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 139(9), pp. 091005 – 091019, 2017. doi: 10.1115/1.4036641

  34. M.S. Tootooni, C. Liu, D. Roberson, R. Donovan, P. Rao, Z. Kong, S.T.S. Bukkapatnam. Online Non-contact Surface Finish Machining using Graph-based Image Analysis. SME Journal of Manufacturing Systems, Vol. 41, pp. 266-276, October 2016. doi: 10.1016/j.jmsy.2016.09.007.

  35. R. Salary, J. Lombardi, M.S. Tootooni, R. Donovan, P. Rao, M. Poliks, P. Borgesen. Computational Fluid Dynamics Modeling and Online Monitoring of Aerosol Jet Printing Process. ASME Transactions, Journal of Manufacturing Science and Engineering, 139(2), pp. 021015-021036, October 2016. doi:10.1115/1.4034591

  36. J. Liu, Omer F. Beyca, P. Rao, Z. Kong, and S. Bukkapatnam. Dirichlet Process Gaussian Mixture (DPGM) Models for Real-Time Monitoring and its Application to Chemical Mechanical Planarization. IEEE Transactions, Automation Science and Engineering, Vol. 14(1), pp. 208-221, 2017. doi: 10.1109/TASE.2016.2599436.

  37. K. Bastani, P. Rao, and Z. Kong. An Online Sparse Estimation-based Classification (OSEC) Approach for Real-time Monitoring in Advanced Manufacturing Process from Heterogeneous Sensor Data. IIE Transactions, Quality and Reliability Engineering, 48(7), pp. 579-598, 2016. doi: 10.1080/0740817X.2015.1122254 Best Paper Award, Invited talk at IISE Conference, 2018. Article highlighted in the June 2016 (Volume 48, Number 9) Issue of the Industrial and Systems Engineer (ISE) Magazine.

  38. P. Rao, Z. Kong, C. Duty, R. Smith, V. Kunc, and L. Love. Assessment of Dimensional Integrity and Spatial Defect Localization in Additive Manufacturing (AM) using Spectral Graph Theory. ASME Transactions, Journal of Manufacturing Science and Engineering, 138(5), pp. 051007, 2015. doi: 10.1115/1.4031574

  39. O. Beyca, P. Rao, Z. Kong, S. Bukkapatnam, and R. Komanduri, Heterogeneous Sensor Data Fusion Approach for Real-time Monitoring in Ultraprecision Machining (UPM) process using non-parametric Bayesian clustering and evidence theory. IEEE Transactions, Automation Science and Engineering, 13(2), pp.1033-1044, 2016. doi: 10.1109/TASE.2015.2447454

  40. P. Rao, J. Liu, D. Roberson, and Z. Kong, and C. Williams. Online Real-time Quality Monitoring in Additive Manufacturing Processes using Heterogeneous Sensors. ASME Transactions, Journal of Manufacturing Science and Engineering. 137(6), pp. 061007, 2015. doi: 10.1115/1.4029823.

  41. P. Rao, S. Bukkapatnam, O. Beyca, Z. Kong, K. Case, and R. Komanduri. A Graph-Theoretic Approach for Quantification Of Surface Morphology and Its Application To Chemical Mechanical Planarization (CMP) Process. IIE Transactions, Quality and Reliability Engineering, 47(10), pp. 1-24, 2015. doi: 10.1080/0740817X.2014.1001927                                    Best Paper Award (Honorable Mention), Invited talk at IISE Conference, 2017. Article highlighted in the September 2015, (Volume 47, Number 6) issue of the Industrial Engineer Magazine (now called Industrial and Systems Engineer)

  42. P. Rao, S. Bukkapatnam, O. Beyca, Z. Kong, and R. Komanduri. Real-time Identification of Incipient Surface Morphology Variations in Ultraprecision Machining Process. ASME Transactions, Journal of Manufacturing Science and Engineering, 136(2), pp. 021008, 2014. doi: 10.1115/1.4026210

  43. P. Rao, M. Bhushan, S. Bukkapatnam, Z. Kong, S. Byalal O. Beyca, A. Fields, and R. Komanduri, Process-Machine Interaction (PMI) Modeling and Monitoring of Chemical Mechanical Planarization (CMP) Process Using Wireless Vibration Sensors. IEEE Transactions, Semiconductor Manufacturing, 27(1), pp. 1-15, 2014. doi: 10.1109/TSM.2013.2293095

  44. S. Bukkapatnam, P. Rao, W-C. Lih, N. Chandrashekeran, and R. Komanduri, Process Characterization and Statistical Analysis of oxide CMP on a Silicon Wafer, Applied Physics (A), 88(4) pp. 785-792, 2007. doi:10.1007/s00339-007-4082-x

  45. S. Bukkapatnam, P.Rao, and R. Komanduri. Experimental Dynamics Characterization and Monitoring of MRR in Oxide Chemical Mechanical Planarization (CMP) Process. International Journal of Machine Tools and Manufacture, 2008, 48(12-13), pp.1375-1386. doi:10.1016/j.ijmachtools.2008.05.006.

  46. Wen-Chen Lih, S. Bukkapatnam, P. Rao, N. Chandrasekharan, R. Komanduri. Adaptive Neuro-Fuzzy Inference System Modeling of MRR and WIWNU in CMP Process with Sparse Experimental Data. IEEE Transactions, Automation Science and Engineering, 5(1), pp. 71 -83, 2008. doi: 10.1109/TASE.2007.911683

  47. S. Bukapatnam, R. Komanduri, H. Yang, P. Rao, W.C. Lih, M. Malshe, L.M. Raff, B. Benjamin, and M. Rockley. Classification of Atrial Fibrillation Episodes from Sparse Electro-Cardiogram Data. Journal of Electrocardiology, 41(4), pp. 292-299, 2008. doi:10.1016/j.jelectrocard.2008.01.004

  48. J.M, Govardhan, S. Bukkapatnam, Y. Bhamare, P. Rao, and V. Rajamani. Statistical analysis and design of RFID systems for monitoring vehicle ingress/egress in warehouse environments. International Journal of Radio Frequency Identification Technology and Applications, 2007, 1(2), pp. 123-146. doi: 10.1504/IJRFITA.2007.013140

Recent Presentations

 
  1. Invited Poster and Student Fellow: Real-time monitoring of surface morphology variations in ultra-precision manufacturing processes, NSF Civil Mechanical and Manufacturing Innovation Conference, Boston, MA, 2012.
  2. Invited Poster and Future Academician Colloquium Invitee: Real-time monitoring of surface morphology variations in ultra-precision manufacturing processes, IIE Industrial Engineering Research Conference, Orlando, FL, 2012.
  3. Future Academician Colloquium Invitee: INFORMS, Phoenix, AZ, 2012.
  4. Invited paper and CIRP student grant: S.T.S Bukkapatnam, P.K. Rao, O.F. Beyca, Z.Kong, and R. Komanduri, Towards Real-time Detection of Incipient Surface Variations in Ultra-Precision Machining Process, 44th  CIRP Conference, University of Wisconsin-Madison, Madison, WI, 2011.
  5. Invited paper: R. Komanduri, S. Bukkapatnam, P.K. Rao and U. Phatak , Experimental Dynamics Characterization and Monitoring of Chemical Mechanical Planarization Process, Invited Paper, CMP-MIC 2007, Freemont, CA.
  6. Invited Poster and Student Fellow: Monitoring of Surface Generation in Ultra-Precision Machining using Multi sensor Fusion, NSF Civil Mechanical and Manufacturing Innovation Conference, Atlanta, GA , 2010.
  7. Invited Poster: Heterogeneous Wireless Sensing and Modeling of Chemical-Mechanical Interactions in Chemical Mechanical Planarization Process for Microelectronic Applications, NSF Nanomanufacturing Workshop, Boston, MA, 2009.
  8. Multiple invited talks at INFORMS conference, 2005, 2010, 2011, 2012, 2013, 2014 (2 talks), and 2015 (2 talks).
  9. Multiple invited talks at ISERC conference, 2012, 2015.
  10. Multiple talks at NAMRC/MSEC, 2015 (2 talks), 2016 (3 talks).