Self-optimizing process planning for helical flute grinding
Self-optimizing process planning for helical flute grinding
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DOI：
10.1007/s11740-019-00908-0
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
B. Denkena;M. Dittrich;V. Böß;M. Wichmann;S. Friebe
中科院分区：
文献类型：
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作者：
B. Denkena;M. Dittrich;V. Böß;M. Wichmann;S. Friebe
螺旋槽的磨削加工是圆柱刀具制造过程中的重要环节。磨削过程定义了所制造刀具的动态性能。并初步确定了表面质量和槽形。因此，找到最佳工艺参数至关重要，特别是在单个刀具的工艺规划中。在工业上，对机器设置进行手动实验。这导致由于机器时间、劳动力和材料成本而导致的高成本。本文提出了一种自寻优、自适应的工艺设计方法，以降低工艺设计成本，提高工艺效果。所开发的方法允许确定最佳的切削速度和进给相对于经济效率和质量的新工具，而无需额外的加工实验。几何运动学切削仿真与经验模型相结合，用于预测过程的结果。经验模型来自机器学习，并随着过程数据的增加而自动改进。应用所提出的方法在一个案例研究中，加工时间可以减少高达38.1%，芯直径偏差高达73.7%。此外，它表明，所提出的方法允许一个不断改进的过程模型。
Grinding of helical flutes is an important step in the process chain of cylindrical tool manufacturing. The grinding process defines the dynamic performance of the manufactured tool. Moreover, the surface quality and flute shape are primarily determined. For this reason, finding optimum process parameters is essential, especially in process planning of individual tools. In the industry, manual experiments are carried out for the machine set up. This leads to high costs due to machine hours, labor and material costs. This paper presents a self-optimizing and adaptable process planning method to reduce costs for process planning and improve the process result. The developed method allows to identify optimum cutting speed and feed with respect to economic efficiency and quality for new tools without additional machining experiments. Geometric-kinematical cutting simulations in combination with empirical models are used to predict the process outcome. The empirical models are derived from machine learning and improved automatically with an increase of process data. Applying the presented method in a case study, the machining time could be reduced by up to 38.1% and the core diameter deviation by up to 73.7%. Moreover, it is shown that the presented methods allow a continuous improvement of the process models.