R&D
❯
Product Improvment
❯Generative design of mechanical parts
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
Generative design of mechanical parts
For:
Organizations, designers, customers, end usersProblem addressed
Create optimized parts following precise mechanical constraints while enabling
cost savings by reducing the amount of material necessary to achieve goals.
Scope of use case
Help mechanical engineers design lighter, strong, and better parts.
Description
Generative design is an iterative design process that
involves a program that generates a certain number of
outputs that meet certain constraints, and a designer that is
possible to fine tune the feasible region by changing
minimal and maximal values of an interval in which a
variable of the program meets the set of constraints, in
order to reduce or augment the number of outputs to
choose from.