R&D
❯
Product Improvment
❯New machine-learning simulations reduce energy need for N95 mask fabrics
Leveraging AI to enhance adhesive quality
For:
Manufacturing industries; suppliers and buyers; environment
Scope:
Batch/continuous/discrete manufacturing (Deployed in 75+ manufacturing lines
in 10+ countries; specifically identify the contributors to quality; predict
potential quality failures).
Goal:
Other
Improvement of productivity of semiconductor manufacturing
For:
Executives of semiconductor manufacturing companies
Scope:
Analysis of data taken from production equipment and improvement of productivity based on the analysis.
Goal:
Other
Generative design of mechanical parts
For:
Organizations, designers, customers, end users
Scope:
Help mechanical engineers design lighter, strong, and better parts.
Optimization of ferroalloy consumption for a steel production company
For:
Steelmaking, steel industry
Scope:
Recommendation for the optimal consumption of ferroalloys by the ladle furnace
treatment during secondary steelmaking.
Goal:
Other
New machine-learning simulations reduce energy need for N95 mask fabrics
For:
Manufacturing companies of high density mask fabrics, in this project 3M specifically.
Goal:
Improved Product Development / R&D
AI solution to calculate amount of contained material from mass spectrometry measurement data
Scope:
Calculating the amount of contained material from mass spectrometry
measurement data using chromatography.
Goal:
Reduce costs
New machine-learning simulations reduce energy need for N95 mask fabrics
For:
Manufacturing companies of high density mask fabrics, in this project 3M specifically.
Goal:
Improved Product Development / R&DProblem addressed
Producing a large number of N95 masks, that have protected the world during the COVID-19 pandemic, is a highly energy intensive process that also demands extreme attention to detail. Producing a high quality product (N95 mask) involves spinning tiny plastic fibers at high temperatures, which for an estimated 300,000 tons of annually melt-blown materials require roughly 245 gigawatt-hours per year of energy.
In conjunction with 3M, the Argonne National Laboratory (part of U.S. Department of Energy) seeks to reduce the energy consumption of this process by 20% by using simulations and machine learning on the Theta supercomputer at the Argonne Leadership Computing Facility (ALCF) with the computational fluid dynamics (CFD) software OpenFOAM and CONVERGE.
Description
The melt blowing process uses a die to extrude plastic at high temperatures. Finding a way to create identical plastic components at lower temperatures and pressures motivated the machine-learning search. By using simulations and machine learning, Argonne researchers can run hundreds or even thousands of use cases, an exponential improvement on prior work.
The simulations provide key insights into the process, a method to assess a combination of parameters that are used to generate data for the machine-learning algorithm. The machine-learning model can then be leveraged to ultimately converge on a design that can deliver the required energy savings.
Raw Data
AI: Perceive
Machine Learning
AI: Understand
Optimize
AI: Act