Evaluation of Deep Autoencoders for Prediction of Adjustment Points in the Mass Production of Sensors

  • Martin LachmannEmail author
  • Tilman Stark
  • Martin Golz
  • Eberhard Manske
Conference paper
Part of the Technologien für die intelligente Automation book series (TIA, volume 11)


In the context of Industry 4.0 the inclusion of additional information from the manufacturing process is a challenging approach. This is demonstrated by an example of calibration process optimization in the mass production of automotive sensor modules. It is investigated to replace a part of a measurement set by prediction. Support-vector regression compared to multiple, linear regression model shows only minor improvements. Feature reduction by deep autoencoders was carried out, but failed to achieve further improvements.


machine learning industry 4.0 deep autoencoder support vector regression feature reduction alignment process production data 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Martin Lachmann
    • 1
    Email author
  • Tilman Stark
    • 1
  • Martin Golz
    • 2
  • Eberhard Manske
    • 3
  1. 1.Robert Bosch Fahrzeugelektrik Eisenach GmbHEisenachDeutschland
  2. 2.Hochschule Schmalkalden, Fakultät InformatikSchmalkaldenDeutschland
  3. 3.Technische Universität Ilmenau, Fakultät MaschinenbauIlmenauDeutschland

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