Surface Partitioning for 3+2-axis Machining: The application of artificial intelligence algorithms for the machining of complex surfaces

Roman, Armando

ISBN 10: 3847301152 / ISBN 13: 9783847301158

Published by LAP LAMBERT Academic Publishing, 2011

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Bibliographic Details

• Title: Surface Partitioning for 3+2-axis Machining: The application of artificial intelligence algorithms for the machining of complex surfaces
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3847301152
• ISBN 13: 9783847301158
• Binding: Paperback
• Condition: Like New
• Book Type: book

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Synopsis

Despite the inbuilt advantages offered by 5-axis machining, the manufacturing industry has not widely adopted this technology due to the high cost of machines and insufficient support from CAD/CAM systems. Companies are used to 3-axis machining and the operators are in many cases not yet ready for 5-axis machining in terms of training and programming. An effective solution for this 5 axis problem is a graduated migration through the use of 3+2-axis machining. The objective of this research is to develop and implement a machining technique that uses the simplicity of 3-axis tool positioning and the flexibility of 5-axis tool orientation, to machine complex surfaces. This work presents the application of well known methods from Pattern Recognition and newly developed methods by the current author that were adapted for surface machining. This work includes an explanation of the procedures required to determine an appropriate tool orientation, feed direction, tool path trajectory and tool parameters for patch-by-patch machining. These parameters are determined independently for each patch and aim at reducing the time required to machine a surface while maintaining the surface specific.

About the Author

Dr. Armando Roman is a graduate from the University of Waterloo and currently the Director of Mechatronics at Tec de Monterrey Campus Guadalajara. His reseach interests are in the application of artificial intelligence methods for manufacturing. He has participated as a faculty advisor for the Baja SAE, HPV, Aero design and Electraton teams.