This site uses cookies in order to improve your user experience and to provide content tailored specifically to your interests. Detailed information on the use of cookies on this website is provided in our Privacy Policy. You can also manage your preferences there. By using this website, you consent to the use of cookies.
The production capacity of the factory is limited by the long throughput time of the x-ray end-of-line test.
Requirements:
Develop algorithm to predict likelihood of manufacturing defects and therefor the need for quality testing by applying closed loop analytic approach
Increase throughput in production by reducing number of necessary x-ray tests
Save time, effort and costs
Our Solution:
Gather process data from the physical equipment and x-ray results of produced pieces (quality labels)
Model multivariate dependencies of process data and x-ray labels by means of supervised machine learning algorithms (binary classification)
Integrate predictive algorithm into Plant-IT system landscape
Continuously re-train the prediction model to increase the accuracy of the predictions
Minimize number of x-ray tests and test only when required to eliminate costly bottleneck
Continuously running algorithms integrated into the production process on-site
Re-trained, up-to-date prediction models
Siemens Amberg produces about 6 million SIMATIC-Products annually. 75% of the value chain is automated, making the factory one of Siemens’ leading manufacturing sites for digitalization.
The project addresses the surface mounted device line with X-ray end-of-line (EoL) testing which takes an X-ray of solder joints of BUS-Connector PINs within a SIMATIC ET200SP Base Unit.
