Dreaming neural networks for adaptive polishing

authored by: 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.
Organisation(s): Institute of Production Engineering and Machine Tools
PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines
Institute of Information Processing
External Organisation(s): Technische Universität Braunschweig
Type: Conference contribution
Pages: 263-266
No. of pages: 4
Publication date: 2020
Publication status: Published
ASJC Scopus subject areas: Instrumentation, Industrial and Manufacturing Engineering, General Materials Science, Environmental Engineering, Mechanical Engineering
Electronic version(s): https://www.euspen.eu/knowledge-base/ICE20379.pdf (Access: Open)