R&D
❯
Product Improvment
❯Optimization of ferroalloy consumption for a steel production company
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
Optimization of ferroalloy consumption for a steel production company
For:
Steelmaking, steel industryGoal:
OtherProblem addressed
Reducing the use of ferroalloys in metallurgical plants while maintaining alloy
quality standards for steel. Improving production efficiency.
Scope of use case
Recommendation for the optimal consumption of ferroalloys by the ladle furnace
treatment during secondary steelmaking.
Description
Datana Smarts application area concerns manufacturing
process optimization. The solution increases equipment
productivity largely removes the human factor, and reduces
energy and material resource consumption.
Joint usage of physico-chemical technological models and
machine learning models cancels mutual disadvantages and
strengthens the advantages of the models.
Datana Smart uses historical data, including:
steel grades specifications;
results of chemical analyses;
chemical composition requirements and standards for
ferroalloy use.
Machine Learning
AI: Understand