Digitalization of brownfield manufacturing assets

How any legacy equipment can profit from data enablement with Augmented Machine Vision (AMV)

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Making the digitalization of brownfield assets possible

Manufacturers relying on proven brownfield machinery, are often faced with the challenge of making their legacy manufacturing equipment ready for the digital future. Thereby, technically complex and expensive updates often stand in the way of using valuable data from machines and operations.

With the Siemens Advanta Augmented Machine Vision (AMV), a complete digitisation layer can be added with just a small investment. Rapid implementation is possible, minimizing  production losses due to smallest possible interventions in the production process.

Would you like to make the digitalization of your brownfield assets possible?

Augmented Machine Vision (AMV): A lightweight vision system for digital image processing and interpretation

The Siemens Advanta Augmented Machine Vision (AMV) is a solution that uses smart cameras to collect detailed and real-time insights into manufacturing processes. As it does not interfere with existing tools and systems, complex setups and expensive upgrades can be avoided, making the replacement of proven legacy equipment obsolete. By using building blocks, the solution also can easily be adapted and extended to individual purposes.

1
Predictive Process

Boost the quality and performance of processes with predictive data models.

2
Self Learning Systems

Expand capabilities of systems like machines or robots by using virtual feedback.

3
Visual Inspection

Automate quality assurance by autonomous object analysis.

4
Localization

Reduce search time and find relevant material and assets area-wide.

QUALITY INSPECTION WITH AUGMENTED MACHINE VISION

Various use cases with Augmented Machine Vision (AMV) help to improve quality analysis by detection, localization, and assessment of multiple defects during production process.

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Improve defect detection

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Exact localization of damage

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Error reduction through deep learning approach and post-processing step

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USE CASE

THE CHALLENGE

Identification of multiple defects in drinking cans

OUR APPROACH

Solving the problem visually with use of Deep Convolution Network to give probability of defect, identify, and localize actual defects

OUR IMPACT

Accuracy of model of around 90% on unknown test data

OUR EXPERTS

Get in touch with our experts to discuss how we can work together to solve any of your challenges.

Marcus Bluhm
Marcus Bluhm
Solution Partner for Commercial Industries EMEA
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Christian Werner
Christian Werner
Development Head of Solution Engineering
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