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Neural network based manufacturability evaluation of free form machining
Marjan Korošec, Jože Balič, Janez Kopač, 2005, original scientific article

Abstract: Most CAD/CAM and computer-aided process planning systems manipulate all geometrical features on the part equally. In the area of free form machining, lack of efficient methodology for assessing the degree of manufacturing pretentiousness of free form features is still noticeable. Developing this methodology inside CAD/CAM systems brings the following benefits to the tool shop praxis: it minimizes the number of set-ups and tool changes and at the same time ensures the right sequence of machining strategies in order to achieve the best possible surface quality in the machining area. Based on this assessment, the CAD/CAM process will also be greatly simplified. When there are an increased number of non-prismatic and non-cylindrical features, this problem is even more exaggerated, and its solution cannot be found in the framework of analytical mathematics. This paper reports a neuro-fuzzy model that uses the concept of "feature manufacturability" to identify and recognize the degree of "pretentiousness-difficulty of machining". The model is created by means of the construction of parametric fuzzy membership functions, based on neural networks learning process. This makes possible simultaneous evaluation of features complexity in a CAD model and manufacturing capability in an environment description model.
Keywords: index of machining complexity
Published: 01.06.2012; Views: 677; Downloads: 47
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Intelligent tool path generation for milling of free surfaces using neural networks
Jože Balič, Marjan Korošec, 2002, original scientific article

Abstract: The presented paper has an intention to show how with the help of Artificial Neural Network (ANN), the prediction of milling tool-path strategy could be made in order to establish which milling path strategy or their sequence will show the best results (will be the most appropriate) at free surface machining, according to set technological aim. In our case the best possible surface quality of machined surface was taken as the primary technological aim. Configuration of used Neural Network (NN) is presented, and the whole procedure is shown on an example of mould, for producing light switches. The verification of machined surface quality, according to average mean roughness, Ra, is also being done, and compared with the NN predicted results
Keywords: neural network, CAD/CAM, CAPP, ICAM, milling strategy
Published: 01.06.2012; Views: 963; Downloads: 60
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