Surface Partitioning for 3+2-axis Machining
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Date
2007-08-01T18:17:14Z
Authors
Roman Flores, Armando
Advisor
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Journal ISSN
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Publisher
University of Waterloo
Abstract
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 technique, 3+2-axis machining, divides a surface into patches and then machines each patch using a fixed tool orientation. The tool orientation and section boundaries are determined to minimize the overall machining time. For each section the tool orientation is different but remains constant while machining this section. The number of patches selected for machining has a direct impact on the machining time. If the number of patches is small, the shape of the tool may vary greatly from that of the surface, which can result in smaller side-step distances. In contrast, a large number of patches leads to a better match between the tool and the workpiece, but it also leads to many re-orientations of the part as the tool moves between patches. Also, if the number of patches is large, the size of the patches will be reduced which will result in shorter tool passes that limit the tools ability to achieve the commanded feed rate. The optimum number of patches is a compromise between increasing the side step associated with large patches and the increase in time due to re-orientation of part and tool movement between patches. To find the optimal partition, a series of simulation tests are conducted to find the partition that would lead to the smallest machining time. 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 and boundary identification. This work also presents the methodology required to generate tool paths for 3+2-axis machining, which 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 specifications. This work presents the surface partitioning scheme and the method of selecting optimum number of partitions along with actual machining experiments. Machining tests on four different surfaces were conducted to demonstrate the efficiency of the proposed technique. The results show that 3+2-axis machine reduced machining times over 3-axis ball nose machining and 5-axis machining using the “Sturz” method. Also, since the tool axis remains fixed during cutting, the tool offers constant feed rates and a better surface finish compared to simultaneous 5-axis.
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Keywords
5-axis, Artificial Intelligence