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Highly Efficient Milling on the Example of Selected Machining Strategies

Zaleski Kazimierz, Matuszak Jakub, Zyśko Andrzej

Advances in Science and Technology. Research Journal

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2020

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Vol. 14, no 1

167--177

EN

The pursuit to achieve a broadly defined optimisation of the manufacturing processes imposes the use of the increasingly innovative machining methods. The increase of the machining efficiency, assuring a high surface quality as well as precision of dimensions and shapes, necessitates the search for new methods to meet the demanding requirements, apart from the development of materials used for the working parts of tools, wear-resistant coatings or improvement of the cutting tool point geometry. One of the methods to improve forming by machining is the optimisation of the machining strategy during the manufacture of the components having complex shapes. The progress in this field is particularly noticeable along with development of the software for machining on multi-axis machines. This article presents the results of tests for the impact of machining strategy on passive force, cutting torque, material removal rate, topography of the obtained surface and the shape of chip resulting from the aluminium alloy milling. The tests were performed by comparison of the classic strategy available in the NXCAM system to the iMachining technology.

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Intelligent prediction of milling strategy using neural networks

Klancnik S., Balic J., Cus F.

Control and Cybernetics

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2010

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Vol. 39, no 1

9-24

EN

This paper presents the prediction of milling tool-path strategy using Artificial Neural Network (ANN), by taking the predefined technological objectives into account. In the presented case, the best possible surface quality of a machined surface was taken as the primary technological aim. This paper shows how feature extraction from a 3D CAD model, and classification using a self-organizing neural network, are done. The experimental results presented in this paper suggest that the prediction of milling strategy using the self-organizing neural network (SOM) is effective.

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Neural network based selection of optimal tool - path in free form surface machining

Korosec M., Kopaz J.

Journal of Automation Mobile Robotics and Intelligent Systems

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2007

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Vol. 1, No. 4

41-50

EN

The purpose of the presented paper is to show how with the help of artificial Neural Network (NN) the prediction of milling tool-path strategies could be performed in order to determine which milling tool - path strategies or their sequences will yield the best results (i.e. the most appropriate ones) of free form surface machining, in accordance with a selected technological aim. Usually, the machining task could be completed successfully using different tool-path strategies or their sequences. They can all perform the machining task according to the demands but always only one of the all possible applied strategies is optimal in terms of the desired technological goal (surface quality in most cases). In the presented paper, the best possible surface quality of a machined surface was taken as the primary technological aim. Configuration of the applied Neural Network is presented and the whole procedure of determining the optimal tool-path sequence is shown through an example of a light switch mould. Verification of the machined surface quality, in relation to the average mean roughness Ra is also being performed and compared with the NN predicted results.