Manufactures & Factories
❯
Product improvement
❯Leveraging AI to enhance adhesive quality
AI solution to quickly identify defects during quality assurance process on wind turbine blades
For:
Manufacturer
Scope:
Detecting defects in products by inspecting non-destructive testing scanning
data.
Goal:
Other
AI decryption of magnetograms
For:
Manufacturer
Scope:
Oil and gas transportation. AI solution to quickly identify defects during the
quality assurance process on a field pipeline.
Goal:
Improve Operation Efficiency
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.
Automatic classification tool for full size core
For:
Manufacturer, geologist
Scope:
Oil and Gas exploration, classification of rock types, oil saturation, carbonate and
fracture according to core images
Goal:
Other
Information extraction from hand-marked industrial inspection sheets
For:
Manufacturing companies, machine inspectors, engineers
Scope:
Localization and mapping of machine zones, arrows and text, to extract
information from manually tagged inspection sheets.
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
Leveraging AI to enhance adhesive quality
For:
Manufacturing industries; suppliers and buyers; environmentGoal:
OtherProblem addressed
Enhance adhesive quality, performance benchmarking.
Scope of use case
Batch/continuous/discrete manufacturing (Deployed in 75+ manufacturing lines
in 10+ countries; specifically identify the contributors to quality; predict
potential quality failures).
Description
The Cerebra IoT signal intelligence platform ingested three
plus years of process data and sensor data regarding plant
operations from temperature, rpm, torque and pressure
sensors that were strapped on to industrial mixers. These are
the mandatory sensors for the operations. Cerebra used its
episode detection algorithms (deep learning) to filter signals
from noise and specifically identify the contributors to
quality (anomaly signatures) that can then be used as signals
to predict quality. It used its proprietary N-dimensional
Euclidian distance-based scoring algorithms to normalize
and present a unified score to the business team. This unified
health score provided the process team with a different lens
to benchmark, specifically target and radically improve
process efficiencies. Cerebra then leveraged its sophisticated
ensemble models to predict potential quality failures,
allowing the operations team to take real-time actions to
control process deviations. The signals identified in the
earlier steps provide model explainability to the end-user for
reasons behind quality deviation.