During the research project, developed path planning algorithms were
implemented by using C++, OpenCL and OpenGL with support of GPU acceleration and performed on Dell Precision M6500 mobile workstation (GPU: NVidia Quadro
FX2800M with 96 CUDA cores @1.5GHz, CPU: Intel i7-820QM, 4 cores @1.73GHz).
Implemented algorithms were used for generation tool paths and G-code programs that
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were tested by performing both simulation in developed simulator and milling on the Okuma MB-46VE CNC milling machine. A set of randomly selected complex 3d models in STL format available on the Internet was used as the input for path planning
algorithms. In this part of the research, project collisions between tool holder, machine components and workpiece were not considered and manually avoided by selecting proper tooling and fixtures (collision avoidance will be described in following chapters during a discussion about 5-axis orientation selection). All milling experiments used 1/8”
flat-end cutting tool for roughing and 1/16” ball-end cutting tool for finishing and were performed with two different types of plastic. Figures on pages 85-87 demonstrate both the simulation and milling results of the performed experiments.
Figure IV-13: Experimental milling results for the “Tiger paw” model
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Figure IV-14: Experimental milling results for the “Sculptures” model
Figure IV-15: Experimental milling results for the “Yoda” model
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Figure IV-16: Experimental milling results for the “Zoo” model
Discussion
In this part of the work, a set of popular algorithms were redesigned in order to implement them on GPU and demonstrate a possibility of using GPU for tool path planning calculations. The experimental simulation and milling results show that the tool trajectories generated by GPU are valid and can be used for actual milling. At the same time GPU implementation of path planning algorithms show that traditional approaches for representing and processing geometry may be not the best choice for GPU and there is a need for replacing them by other GPU-friendly geometry representations and algorithms.
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It is important to notice that the height map data structure used allows path planning for 3-axis machines with some limitations only. There are known techniques that allow using multiple height maps for 3+2-axis milling but there is no way to do more general 5-axis path planning without significant changes to a geometry representation and algorithms. Although a height map is not suitable for 5-axis milling, it is one of the best candidates for geometry representation that can be easily processed in parallel, and it may make sense combining more complicated data structures for 5-axis milling with some special cases like pocket milling or initial roughing operations processed with a height map.
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V. 5-AXIS MACHINING SIMULATION
5-axis CNC milling is not as popular today as the 3-axis milling mainly due to its complexity. Two additional degrees of freedom make the tool path planning process much more complicated. They also significantly increase a chance of a collision in case of a path planning error. And from the mechanical point of view it is much harder to make 5-axis machines as rigid as their 3-axis analogs. Although two additional axes bring a lot of problems and complexity, they also bring a lot of freedom and possibilities. For example, the 5-axis milling may significantly decrease a number of setups and related positioning errors. And what is most important is that 5-axis milling can significantly decrease the milling time and produce better surface quality by using a more efficient tool orientation and a shorter tool path. But in order to use benefits of the 5-axis CNC milling the tool path planning process has to be automated or at least significantly simplified.
This chapter discusses one of the most important parts of the tool path planning process – the milling simulation. The milling simulation is used for two different areas.
From one side it is an important component of a tool path planning process since continuous simulation allows selection of a safe tool orientation and save time by processing only areas that contains material. From the other side, the simulation process is extremely important for verification of a generated tool path in safe virtual
environment before using it on a real machine.
The problem with 5-axis simulation is its complexity from the mathematical and the computational points of view. As it was showed in previous chapters the
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computational problem can be solved by using GPUs for calculations. But in the case of the 5-axis simulation there are no known highly parallel algorithms and data structure that can be used in GPGPU approach. The 5-axis workpiece cannot be naturally
represented by a height map as it was done in the 3-axis milling and there are no known geometry representations that can be efficiently edited in parallel on GPUs, provide good performance and memory usage. As a result there is a need for a specially designed data structure and highly parallel simulation algorithms especially designed for 5-axis milling simulation.
During this research project, a new volume representation was developed as well as a set of algorithms for the 5-axis CNC milling simulation that can run on multi-GPU systems. Although the described system is designed for 5-axis CNC milling, it is quite general and can also be used for simulation of multi-axis machines with more than 5 axes.
This chapter describes the developed volume representation, the used data structure, rationale behind its design and discusses its properties. It also provides information about developed highly parallel algorithms used for rendering and editing and the algorithms performance evaluation. Finally it describes experimental simulation results and discusses the efficiency of the performance scalability on multiple GPUs.