Abstract
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.
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Lachmann, M., Stark, T., Golz, M., Manske, E. (2020). Evaluation of Deep Autoencoders for Prediction of Adjustment Points in the Mass Production of Sensors. In: Beyerer, J., Maier, A., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 11. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59084-3_2
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DOI: https://doi.org/10.1007/978-3-662-59084-3_2
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