Digital twins are breathing life and innovation into increasingly more areas of manufacturing as well as solving challenges for machine shops of all sizes. With the skilled labor shortage and an ongoing effort to reshore high-tech manufacturing to the U.S., digital twins have a lot to offer. Digitizing the machine and process creates a deep understanding of the CNC, programs, process, tooling and setup.
Additionally, with competition ramping up, shops will need to increase and optimize their operations by creating higher efficiencies. Digitalizing traditional manufacturing processes have the potential to make operations more efficient by proving out production processes in the virtual world.
Finally, the enemy of profitability is uncertainty, and with the current state of a globalized economy, uncertainty is almost a guarantee. Good management, via digital twins, can help guard against hiccups and put measures in place that maximize your productivity.
A digital twin of a machine tool is a virtual representation of the machine connected with a virtual control fed with real-world configuration data. This takes model and parametric-based simulation to a new level because one can optimize and prove out machine tool setups more accurately than ever.
Typically, CAM systems and post-processors do not have any direct knowledge of the machine, such as kinematics, available G-codes or if someone changes parameters. CAM-based simulation or general G-code simulation packages cannot show how those changes will affect the program or cycle time because there is no digital twin of the CNC or direct communication to the specific machine controller.
Manual processes are generally used to set up CAM or G-code simulation, so in many cases the program is not optimized for the individual machine or the program fails to take advantage of many modern programming features. The program may not even run on the target machine.
Another issue shops face is their posts and programs are often not optimized for the machines or tooling. For example, the operator may run tools with lower feedrates, perhaps because of concerns over running the machine at the programmed feedrates. This adds up to lost time, lost productivity and low efficiency.
On the other hand, virtualization can enable interoperability between the CNC and CAM system. This helps post the program based on the features the CNC and/or machine supports. Once the program is posted, a virtualized environment can be created beyond just simple simulation because the basis uses the machine kinematics and the real CNC parameters. CAM and G-code simulation can only take the testing so far because the PC has no knowledge of the CNC setup. Using a digital twin to prove out the actual CNC and machine setup will greatly reduce the chance for error.
Digital twins can also help meet demand for more accurate components supplied to high-tech industries that require products with high tolerances. As machining becomes more complex, having the most efficient processes is crucial. Many traditional methods waste valuable production time, such as setup procedures that create bottlenecks because machines are taken out of production. Digital twins allow the testing of programs in a virtualized environment. The virtual CNC accurately follows the actual programmed cutter paths as well as produces the real-world cycle time. When connected to a virtualized machine that matches the kinematics exactly, the digital twin enables accurate verification without ever interrupting the current process that the machine is running. Plus, proving out the process in the office allows the machine to keep operating.
Because digital twins are a replica of a machine tool, training on a digital twin is an invaluable tool to close the CNC skills gap and teach job-shop essentials like part programming and machine tool operation. Digital twins offer users hands-on, realistic machining experience and training without taking an actual machine out of production.
FANUC America has partnered with ModuleWorks to develop workforce development software as well as a new robust part programming suite, NC Reflection Studio, which helps shops with G-code editing, simulation and program verification. All this offers an arsenal of tools to create skilled machinists as well as optimize machine shops. This end-to-end digitalization will then truly unlock the power of Industry 4.0 for the CNC industry.
This year’s 22 awardees were selected based on their diverse manufacturing backgrounds, technology advancements/improvements and state-of-the-art research. The 2022 award namesake is 2017 SME President Sandra L. Bouckley, FSME, P.Eng., former SME CEO and a previous vice president of manufacturing systems, supply chain management and lean at GKN Driveline Americas.
While membership in SME is not required for this recognition, each of the 2022 Sandra L. Bouckley Outstanding Young Manufacturing Engineers are part of the SME community, having been members prior to their selection:
Bruno Azeredo, PhD, Arizona State University, Tempe, Ariz.
Wen Chen, PhD, University of Massachusetts Amherst, Amherst, Mass.
Xu Chen, PhD, University of Washington, Seattle
Nancy Diaz-Elsayed, PhD, University of South Florida, Hillsborough County, Fla.
Amy Elliott, PhD, Oak Ridge National Laboratory, Oak Ridge, Tenn.
Thomas Feldhausen, PhD, Oak Ridge National Laboratory, Knoxville, Tenn.
Kelvin Fu, PhD, University of Delaware, Newark, Del.
Michael Gomez, PhD, MSC Industrial Supply Co., Knoxville, Tenn.
Jinah Jang, PhD, Pohang University of Science and Technology, Pohang, South Korea
Bo Jin, PhD, University of Southern California, Los Angeles
Venkata Charan Kantumuchu, Electrex Inc., Edmond, Okla.
Geoff Karpa, Lockheed Martin Aeronautics Co., Fort Worth, Texas
Vipin Kumar, PhD, Oak Ridge National Laboratory, Knoxville, Tenn.
Megan McGovern, PhD, PE, General Motors Global Research & Development, Detroit
Laura Pahren, Procter & Gamble Co., Mason, Ohio
Kyle Saleeby, PhD, Oak Ridge National Laboratory, Knoxville, Tenn.
Ryan Sekol, PhD, General Motors Research & Development, Warren, Mich.
Xuan Song, PhD, University of Iowa, Iowa City, Iowa
Peng “Edward” Wang, PhD, University of Kentucky, Lexington, Ky.
Sarah Wolff, PhD, Texas A&M University, College Station, Texas
Yang Yang, PhD, San Diego State University, San Diego
Xiaowei Yue, PhD, Virginia Tech, Blacksburg, Va.
SME has highlighted the accomplishments of over 470 young manufacturing engineers—and their impact on manufacturing—for over four decades through this award. SME will welcome nominations for the 2023 Outstanding Young Manufacturing Engineers Award by Aug. 1 at sme.org/oyme.
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