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Applications of Genetic Algorithms in Process-Planning: Tool Sequence Selection for 2.5D Pocket Machining

机译:遗传算法在过程规划中的应用:2.5D口袋加工的刀具序列选择

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Rapid tool change mechanisms in modern CNC machines have enabled the use of multiple tools (sequence of tools) to machine a given pocket. Larger diameter tools that have higher material removal rates are used to clear large open spaces, smaller tools are used for clean up. The challenge lies in selecting that particular combination of tools that minimizes total cost. Previously, we developed algorithms based on network optimization to find the best tool sequence given a list of cutters, cutting parameters and pocket geometry. The formulation was based on certain assumptions that did not account for tool holder geometry. It also required the evaluation of all possible tool-pair combinations for a given tool set. This can get time consuming if the tool set is large. In this paper, we present a genetic algorithm based method to select optimal tool sequences. The algorithm was implemented and bench marked against the graph algorithm. We have found that the GA based method is able to find a near optimal tool sequence without evaluating up to 30% of all possible tool-pairs.
机译:现代CNC机器中的快速刀具改变机制使得能够使用多个工具(工具序列)来为给定的口袋加工。具有更高的材料去除速率的较大直径工具用于清除大型开放空间,较小的工具用于清理。挑战在于选择选择最小化总成本的特定工具组合。以前,我们开发了基于网络优化的算法,找到了刀具,切割参数和口袋几何列表的最佳工具序列。制剂基于不考虑工具架几何形状的某些假设。它还需要评估给定工具集的所有可能的工具对组合。如果工具集很大,这可能会耗时。在本文中,我们介绍了一种基于遗传算法来选择最佳工具序列。实现了算法,并标记为图形算法的基准。我们发现GA基础方法能够在不评估最多30%的所有可能的工具对的情况下找到近最佳刀具序列。

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