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Feature Selection for Trouble Shooting in Complex Assembly Lines

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84138

Pfingsten,  T
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83967

Herrmann,  DJL
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Pfingsten, T., Herrmann, D., Schnitzler T, Feustel, A., & Schölkopf, B. (2007). Feature Selection for Trouble Shooting in Complex Assembly Lines. IEEE Transactions on Automation Science and Engineering, 4(3), 465-469. doi:10.1109/TASE.2006.888054.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CC99-B
Zusammenfassung
The final properties of sophisticated products can be affected by many unapparent dependencies within the manufacturing process, and the products’ integrity can often only be checked in a final measurement. Troubleshooting can therefore be very tedious if not impossible in large assembly lines. In this paper we show that Feature Selection is an efficient tool for serial-grouped lines to reveal causes for irregularities in product attributes. We compare the performance of several methods for Feature Selection on real-world problems in mass-production of semiconductor devices. Note to Practitioners— We present a data based procedure to localize flaws in large production lines: using the results of final quality inspections and information about which machines processed which batches, we are able to identify machines which cause low yield.