Dreaming neural networks for adaptive polishing

verfasst von

Marc André Dittrich, Bodo Rosenhahn, Marcus Magnor, Berend Denkena, Talash Malek, Marco Munderloh, Marc Kassubeck

Abstract

Mechanical polishing is an important step within the process chain of manufacturing workpieces with high requirements regarding the surface quality, e.g. for optical components. The resulting surface quality depends on several parameters, e.g. the process parameters, the workpiece material, the initial surface roughness and the tool condition. Thus, finding process parameters that lead to the desired surface roughness can be regarded as a complex optimization problem. For this purpose, an artificial neural network (ANN) has been designed and trained with data from polishing experiments. Using a dreaming network approach, the ANN has been enabled to suggest appropriate process parameters under consideration of the initial roughness of the workpiece and the tool condition. The validation experiments showed that the process parameters suggested by the neural network led in 72% to the target roughness within a standard deviation.

Organisationseinheit(en)

Institut für Fertigungstechnik und Werkzeugmaschinen
PhoenixD: Simulation, Fabrikation und Anwendung optischer Systeme
Institut für Informationsverarbeitung

Externe Organisation(en)

Technische Universität Braunschweig

Typ

Aufsatz in Konferenzband

Seiten

263-266

Anzahl der Seiten

4

Publikationsdatum

2020

Publikationsstatus

Veröffentlicht

ASJC Scopus Sachgebiete

Instrumentierung, Wirtschaftsingenieurwesen und Fertigungstechnik, Allgemeine Materialwissenschaften, Environmental engineering, Maschinenbau

Elektronische Version(en)

https://www.euspen.eu/knowledge-base/ICE20379.pdf (Zugang: Offen)