Manufactures & Factories
❯
Product improvement
❯AI solution to quickly identify defects during quality assurance process on wind turbine blades
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
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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
AI solution to quickly identify defects during quality assurance process on wind turbine blades
For:
ManufacturerGoal:
OtherProblem addressed
To find an accurate and efficient solution to detect defects without compromising the detection of in-material damage and risking a loss in
reputation.
Scope of use case
Detecting defects in products by inspecting non-destructive testing scanning
data.
Description
The manufacturer produces over five thousand wind turbine
blades every year for use in on/offshore wind farms. Each
blade can be up to 75 m in length and takes a highly skilled
professional quality controller up to 6 h to evaluate the
ultrasonic testing (UT) scanning in the quality assurance
process. This is because the structure can contain multiple
defect types, including how fiberglass can wrinkle during the
production process. This has the potential to be catastrophic
if this makes the blade crash during operation. The
manufacturer is required to put each wind turbine blade
through a stringent quality assurance process. Any defects
when a blade is in operation can not only prove catastrophic
but also inflict major damage to the companys reputation.
Working with the AI solution provider together they co-
created an AI solution that can automatically detect defects
through deep learning capabilities. It achieved high coverage
(more than 95 %) of various defects and reduced evaluation
time of each non-destructive testing scanning by 80 %.
Another method featured in the AI solution is "imagification"
which transforms raw data into image data based on RGB
where deep learning-based image recognition can be applied
effectively. Quality controllers can focus their efforts on
suspicious areas and disregard all clean data; humans are
only expected to examine the blades that are flagged by the
AI system. With five thousand blades produced every year,
that adds up to a saving of almost 32 000 m-h, which
translates into significant cost savings, reduced production
lead times, and increased productivity. Today, there is a
shortage of ultrasonic engineers/inspectors. This solution
means the same inspector can do four to five blades per day
instead of one previously.
Raw Data
AI: Perceive