Simulation of a complex optical polishing process using a neural network

  • Authors:

  • Elmar Pitschke;Markus Schinhaerl;Rolf Rascher;Peter Sperber;Lyndon Smith;Richard Stamp;Melvyn Smith

  • Affiliations:

  • Department of Mechanical Engineering, University of Applied Sciences, Edlmairstr. 6+8, 94469 Deggendorf, Germany;Department of Mechanical Engineering, University of Applied Sciences, Edlmairstr. 6+8, 94469 Deggendorf, Germany;Department of Mechanical Engineering, University of Applied Sciences, Edlmairstr. 6+8, 94469 Deggendorf, Germany;Department of Mechanical Engineering, University of Applied Sciences, Edlmairstr. 6+8, 94469 Deggendorf, Germany;University of the West of England, Coldharbour Lane, Bristol, UK;University of the West of England, Coldharbour Lane, Bristol, UK;University of the West of England, Coldharbour Lane, Bristol, UK

  • Venue:
  • Robotics and Computer-Integrated Manufacturing
  • Year:
  • 2008

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Abstract

Most modern manufacturing processes change their set of parameters during machining in order to work at the optimum state. But in some cases, like computer-controlled polishing, it is not possible to change these parameters during the machining. Then usually a standard set of parameters is chosen which is not adjusted to the specific conditions. To gather the optimum set of parameters anyway simulation of the process prior to manufacturing is a possibility. This research illustrates the successful implementation of a neural network to accomplish such a simulation. The characteristic of this neural network is described along with the decision of the used inputs and outputs. Results are shown and the further usage of the neural network within an automation framework is discussed. The ability to simulate these advanced manufacturing processes is an important contribution to extend automation further and thus increase cost effectiveness.