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Analatom Inc SBIR Phase II Award, November 2019

A SBIR Phase II contract was awarded to Analatom in November, 2019 for $750,000.0 USD from the U.S. Department of Defense and United States Air Force.

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AbstractTimelineTable: Further ResourcesReferences
sbir.gov/node/384315
Is a
SBIR/STTR Awards
SBIR/STTR Awards

SBIR/STTR Award attributes

SBIR/STTR Award Recipient
Analatom
Analatom
1
Government Agency
U.S. Department of Defense
U.S. Department of Defense
1
Government Branch
United States Air Force
United States Air Force
1
Award Type
SBIR1
Contract Number (US Government)
FA8571-20-P-00131
Award Phase
Phase II1
Award Amount (USD)
750,0001
Date Awarded
November 15, 2019
1
End Date
November 15, 2021
1
Abstract

As smart manufacturing has evolved more machines are equipped with smart sensors and meshed with Internet-of-Things. As manufacturing process becomes more complex, more difficulty comes along to clear the data and formulate the right problems to model. Fast reliable industrial inspection is key challenge in manufacturing scenarios. Surface inspection is usually inspected employing machine vision and image processing techniques to detect surface defect for enhanced product quality. However, flexible configuration in modern manufacturing system can shift production from one product to another quickly. Therefore, manually designed features in traditional machine learning technique may lead to insufficient or unsatisfactory inspection performance in complex surface scenarios or dynamic changing process. Addressing these challenges Analatom proposes using deep learning to learn high-level generic features that apply to wide range of textures or difficult-to-detect defects cases. Transfer-learning can extract the knowledge from one source task and then applies the learned knowledge to different but related task. It employs pre-trained deep learning model from relevant task for model initialization and fine-tuning enabling knowledge reuse and updating as transferred deep learning. This allows building inspection systems for novel manufacturing inspection tasks and transitioning to inspect new asset types rapidly and with little skilled oversight from user.

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Further Resources

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References

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