North Carolina Consortium for Self-Aware Machining and Metrology (CSAM)
UNC Charlotte: Dr. Tony Schmitz (PI), Dr. Harish Cherukuri, Dr. Matt Davies, Dr. Ed Morse, Dr. Tom Suleski, Dr. Joshua Tarbutton
NCSU: Dr. Noel Greis, Dr. Monica Nogueira
Fayetteville State University: Dr. Sambit Bhattacharya
Oak Ridge National Laboratory: Dr. Scott Smith
Universitat Politècnica de València (Spain): Dr. Miguel Angel Selles Canto, Dr. Elena Perez-Bernabeu
ATI Specialty Materials, AURA Technology, Blaser Swisslube, Carl Zeiss Industrial Metrology, Caterpillar, DP Technology Corp., Exactech, FLIR Optical Components Group, GE Global Research Center, GE Aviation (Asheville, West Jefferson, and Wilmington), GOM Americas, Intel Corporation, Manufacturing Laboratories, Inc., National Institute of Standards and Technology (NIST), North Carolina Department of Commerce Office of Science, Technology & Innovation, Okuma America Corp., OptiPro, Opto Alignment, OptoMill Solutions, PolylmerPlus, Praemo, Siemens PLM Software, Inc., Structure Medical, and Third Wave Systems
- CSAM will generate new knowledge in the application of artificial intelligence to high precision part manufacture and measurement. Specifically, it will enable self-aware operation, or the ability of a production or measuring machine to understand its current state and surroundings and respond accordingly.
- The innovation is the combination of data-driven and physics-based models to provide hybrid physics-guided data learning approaches that improve the accuracy, physical consistency, traceability, and generalizability of model predictions over traditional data-driven and physics-based methods.
- CSAM will establish a disruptive capability that redefines manufacturing from the shop floor to the enterprise level across the entire global economy. The research efforts will leverage existing team expertise in artificial intelligence, machining process modeling, measurement science, design and ultraprecision machining of freeform optics, and vision systems.
- The research will address the current needs of the US manufacturing base, while leveraging multiple strategic partnerships to ensure technology transfer to industry and the long-term sustainability of the effort.
- Physics-Guided Data Learning Models (Greis, Nogueira, Bhattacharya, Cherukuri)
- Chatter Avoidance (Schmitz, Cherukuri, Greis, Nogueira, Bhattacharya, Selles, Perez-Bernabeu)
- Ultra-Precision Machining of Freeform Optics (Davies, Suleski, Greis, Nogueira, Schmitz)
- Semantic Data Management for Manufacturing and Metrology (Morse, Greis)
- Machining Force Diagnosis (Tarbutton, Schmitz)
- Cherukuri, H., Perez-Bernabeu, E., Selles, M.A., and Schmitz, T., 2019, Machining Chatter Prediction using a Data Learning Model, Journal of Manufacturing and Materials Processing, Special Issue on Machine Tool Dynamics, submitted.
- Cherukuri, H., Perez-Bernabeu, E., Selles, M.A., and Schmitz, T., 2019, A Neural Network Approach for Chatter Prediction in Turning, Procedia Manufacturing, accepted.
- Sizemore, N., Davies, M., Nogueira, M., Greis, N., and Schmitz, T., 2019, Machine Learning Model for Surface Finish in Ultra-Precision Diamond Turning, Model-Based Enterprise Summit 2019, April 1-4, Gaithersburg, MD.
- Greis, N., Nogueira, M., Schmitz, T., and Dillon, M., 2019, Manufacturing-Uber: Intelligent Operator Assignment in a Connected Factory, 9th IFAC Conference on Manufacturing Modelling, Management and Control (MIM 2019), August 28-30, 2019, Berlin, Germany, accepted.
- AI 101 provides an introduction to artificial intelligence as described by the UNC ROI team.
- DoE report on Basic Research Needs for Scientific Machine Learning
- CSAM reference library
- CSAM online news story
- Hamidreza Aryan, UNC Charlotte PhD student, is studying the relationship between optical surface finish and imaging quality.
- Michael Gomez, UNC Charlotte PhD student, is studying force measurement and analysis in machining operations.
- Nate Rumph, UNC Charlotte BSME student, is completing a literature review on the application of artificial intelligence to machining operations.
- Nicholas Sizemore, UNC Charlotte PhD student, is studying the application of data learning models to surface finish prediction in diamond turning.
- Jessie Jiang, UNC Charlotte PhD student, is studying machine learning algorithms for machining stability
- Robert Deal, FSU Computer Science undergraduate student
- Kevin Kabbes, FSU Computer Science undergraduate student
- Jeremiah Prieto, FSU Computer Science undergraduate student
- Dalisha Rivera-Rodriguez, FSU Computer Science undergraduate student
2019 Annual Meeting
We will hold our annual meeting in Duke Centennial Hall (DCH) at UNC Charlotte on Tuesday, May 14 and Wednesday, May 15. DCH is building 57 on the campus map. The agenda is provided here.
Day 1, Tuesday, May 14, 2019
|8:00 am-8:45 am||Coffee and participant registration (name badges will be provided), 345 DCH|
|9:00-9:15 am||Welcome, introductions, and agenda (Tony Schmitz), 345 DCH|
|9:15-10:00 am||AI-ML introduction 1 (Noel Greis)|
|10:15 – 10:45 am||Coffee break, 345 DCH|
|10:45-11:15 am||AI-ML introduction 2 (Sambit Bhattacharya, physics-guided data learning)|
|11:30 am-12:30 pm||Round table discussion moderated by Ed Morse (Dan Frayssinet, DP Technology Corp.; Moneer Helu, NIST; Wolfgang Rohde, Siemens Product Lifecycle Management Software Inc.; Dale Lombardo, GE GRC; Pete Zelenski, MMS), 345 DCH|
|12:30-1:30 pm||Box lunch, 345 DCH|
|1:30-2:00 pm||Research presentation 1 (Harish Cherukuri, ANN machining stability)|
|2:15-2:45 pm||Research presentation 2 (Matt Davies, diamond turning surface finish)|
|3:00-3:30 pm||Break with refreshments, 345 DCH|
|3:30-4:00 pm||Research presentation 3 (Tony Schmitz, machining process modeling)|
|4:30-5:30 pm||Poster session (all students present, poster for each ROI project and related projects), DCH lab hallway|
|5:30 pm||Laboratory tours (Matt Davies)|
|Dinner on your own|
Day 2, Wednesday, May 15, 2019
|8:00-8:30 am||Arrival and coffee, 345 DCH|
|8:45-9:00 am||Welcome and agenda (Tony Schmitz, Monica Nogueira, CSAM reference library), 345 DCH|
|9:00-9:30 am||Research presentation 4 (Noel Greis, M-Uber)|
|9:45-10:15 am||Industrial case study (Jaydeep Karandikar, GE GRC, tool life)|
|10:30-11:30 am||Round table discussion moderated by Joshua Tarbutton (John Foltz, ATI; Eric Strong, Aura Technologies, LLC; Mike Vogler, Caterpillar Inc.; Adam Hamielec, Okuma America; Andy Henderson, Praemo)|
|11:30-11:45 am||Summary and final instructions including online feedback (Tony Schmitz)|
Round table participants
Eric Strong, Manager, AI Systems, AURA Technologies, LLC
Eric’s past work includes the development of machine learning algorithms for condition-based maintenance in multiple industries, including nuclear plants, naval vessels, aircraft, and industrial plants. As technical lead at the DEI Group, he supervised the company’s diagnostic and prognostic models for predictive maintenance. While a senior data scientist at Honeywell Aerospace, he led multi-disciplinary teams in developing predictive algorithms for commercial aircraft. At Honeywell, Eric was a leader in the CBM and Deep Learning technical councils, and his team won the 2018 ANNY Excellence in Analytics award. Currently, Eric is Manager of AI Systems at AURA Technologies, which is quickly becoming a focal point for artificial intelligence (AI) development across the DoD, as the company with the most new-start AI programs of any other company in the DoD.
Andy Henderson, VP of Engineering, Praemo Inc.
Andy’s efforts at Praemo are focused on delivering productivity-improving insights from manufacturer data using Artificial Intelligence analysis tools. Prior to joining Praemo, he earned a B.S. in mechanical engineering from Georgia Tech and a Ph.D. in automotive engineering from Clemson University. He has worked at Caterpillar and two different divisions of GE, including a period as the Industry Solutions Director for Heavy Industry Manufacturing within GE Digital. He stays active in the manufacturing community at large through university engagement and the Society of Manufacturing Engineers (SME). He was a recipient of SME’s Outstanding Young Manufacturing Engineer award in 2016.
There is a $25 registration fee to cover food. Follow this link to register. Because the room capacity is limited, registration will be closed after the first 100 registrants.
UNC Charlotte is accessible from Charlotte Douglas International Airport. Follow this link for travel details. A list of hotels near the campus is provided.
There are many restaurants with a mile of the university. Follow this link for options.
We encourage the participation of industry and government partners. To learn more about collaborative opportunities, contact Dr. Tony Schmitz, firstname.lastname@example.org, (704) 687-5086.