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Progressive segmentation for MRR-based feed-rate optimization in CNC machining

机译:渐进式细分,用于基于CNC加工的基于MRR的进给速度优化

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Keeping a constant cutting force in CNC machining is very important for obtaining better stability of cutting operation and improving topography, texture and geometry of the machined surface. This paper presents a feed-rate optimization approach based on Material Removal Rate (MRR). Given a tool-path with predefined feed-rates, the geometry of raw material, and the shape of cutter, the histogram of MRR in very fine resolution can be efficiently computed by using a GPU-based geometric modeling kernel. Starting from the evaluation given on the finest histogram of MRR, error-controlled subdivision algorithms are developed to progressively segment the tool-path into user-specified number of sub-regions. Different feed-rates are assigned to different sub-regions so that nearly constant MRR can be achieved while keeping the shape of the given tool-path unchanged. Experimental tests taken on real examples verify the effectiveness of this method.
机译:在CNC加工中保持恒定的切削力对于获得更好的切削操作稳定性以及改善加工表面的形貌,纹理和几何形状非常重要。本文提出了一种基于材料去除率(MRR)的进给率优化方法。给定具有预定义的进给速率,原材料的几何形状和刀具形状的刀具路径,可以使用基于GPU的几何建模内核高效地计算出非常精细的MRR直方图。从对MRR的最佳直方图进行评估开始,开发了误差控制的细分算法,以逐步将刀具路径细分为用户指定的子区域数量。将不同的进给速率分配给不同的子区域,以便在保持给定刀具路径形状不变的情况下获得几乎恒定的MRR。在真实示例上进行的实验测试证明了该方法的有效性。

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