R&D
❯
Product Improvment
❯AI solution to calculate amount of contained material from mass spectrometry measurement data
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
AI solution to calculate amount of contained material from mass spectrometry measurement data
Goal:
Reduce costsProblem addressed
To find an accurate and efficient solution to calculating the amount of contained
material without dependence on individuals.
Scope of use case
Calculating the amount of contained material from mass spectrometry
measurement data using chromatography.
Description
Technology was developed that utilizes AI to process the vast
amounts of data used in analyzing the measurement results,
which are essential to analytical processes, acquired from
mass spectrometers.
Mass spectrometers are used for research and quality
control in various areas such as the establishment of early
detection techniques for diseases and the measurement of
residual pesticides in foods, and because of improvements in
sensitivity and speed, the amount of data acquired is
enormous. As a result, the data analysis step called "peak
picking" has become a bottleneck in the workflow. Complete
automation is difficult and to some extent manual
adjustments are required. Therefore, there are differences in
analysis accuracy depending on each operator and there is a
possibility that analytical results can be affected by each
operator's practices and data alterations. In recent years,
automated data analysis with high accuracy that eliminates
this kind of dependence on individuals is now demanded in
the fields of healthcare and new drug development.
To solve this issue using AI, the three companies investigated
the application of deep learning, a neural network
technology that imitates brain neurons. Arising to confront
this process were two problems: 1) insufficient training data;
and 2) learning can not proceed when analytical equipment
output data was input, as is, into the deep learning network.
Technologies to produce extra data to compensate for the
lack of training data and to convert the analysis equipment
output features into images were developed. Moreover, the
companies developed the feature extraction technology to
learn the analytical skills of experienced analysts. By doing
this, the deep learning network was able to learn from the
over 30 000 items of generated training data. Compared with
manual peak picking results by an experienced operator, the
automated peak picking results using AI had a false detection
rate of 7 % and an undetected rate of 9 %. These results
indicate that an automated peak picking can compare
favorably with a peak picking by an experienced operator.
Raw Data
AI: Perceive