NDLI: Methods to Rationalize the Generation of Parameter Technologies in Sinking EDM

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Juhr, Henrik Haas, Ru¨diger Ku¨nanz, Klaus
Copyright Year	2011
Abstract	The demands on parameter technologies in Sinking EDM are very high. In each processing step, a certain surface roughness has to be achieved in very precise manner. At the same time removal rate (productivity) and relative wear (costs) have to achieve advantageous values. The generation of these parameter technologies is time-consuming and expensive, since many influencing factors affect the target figures. The procedural methods in practice are often unsystematic and ineffective. The technological possibilities of machine and process configuration are very often not used. This publication deals with methods designed to effectively optimize the target figures. At the same time, effort and time spent for generating parameter technologies are reduced. Continuous parameter technologies are used for the generation of discrete parameter technologies. Two different types of continuous mappings are compared: Nonlinear regression functions and artificial neural networks (ANN). Model accuracy and the handling properties of these variants are evaluated in this context. A variety of methods for assessing the adequacy of mappings are discussed. A new, problem-oriented experimental design method for EDM-processes is also described, focusing on two aspects: a reduction of the experimental effort and the achievement of a sufficient accuracy of the continuous mappings at the same time. In order to achieve these characteristics, different scale methods and transformation methods are discussed, as well. As a result, a problem-adapted CAP-software solution is presented. Nonlinear regression functions as well as ANNs for the continuous parameter technologies can be generated, visualized and evaluated within this software. In a next step, software functions are implemented, allowing the technologist to derive optimal pulse parameter series for processing. The application of the presented methods is first of all useful for manufacturers of EDM die sinking machines.
Sponsorship	Manufacturing Engineering Division
Starting Page	407
Ending Page	415
Page Count	9
File Format	PDF
ISBN	9780791844304
DOI	10.1115/MSEC2011-50198
Volume Number	ASME 2011 International Manufacturing Science and Engineering Conference, Volume 1
Conference Proceedings	ASME 2011 International Manufacturing Science and Engineering Conference
Language	English
Publisher Date	2011-06-13
Publisher Place	Corvallis, Oregon, USA
Access Restriction	Subscribed
Subject Keyword	Nonlinear regression functions Artificial neural networks (ann) Parameter technologies Sinking electro discharge machining (sedm) Computer aided planning (cap) Electrical discharge machining Artificial neural networks
Content Type	Text
Resource Type	Article

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