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Three-dimensional modeling of coordinate measuring machines probing accuracy and settings using fuzzy knowledge bases: Application to TP6 and TP200 triggering probes

机译:使用模糊知识库的坐标测量机三维建模,以探测精度和设置:在TP6和TP200触发探针上的应用

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One of the fundamental elements that determines the precision of coordinate measuring machines (CMMs) is the probe, which locates measuring points within measurement volume. In this paper genetically generated fuzzy knowledge based models of three-dimensional (3-D) probing accuracy for one- and two-stage touch trigger probes are proposed. The fuzzy models are automatically generated using a dedicated genetic algorithm developed by the authors. The algorithm uses hybrid coding, binary for the rule base and real for the database. This hybrid coding, used with a set of specialized operators of reproduction, proved to be an effective learning environment in this case. Data collection of the measured objects' coordinates was carried out using a special setup for probe testing. The authors used a novel method that applies a low-force high-resolution displacement transducer for probe error examination in 3-D space outside the CMM measurement. The genetically generated fuzzy models are constructed for both one stage (TP6) and two stage (TP200) types of probes. First, the optimal number of settings is defined using an analysis of the influence of fuzzy rules on TP6 accuracy. Then, once the number of settings is obtained, near optimal fuzzy knowledge bases are generated for both TP6 and TP200 triggering probes, followed by analysis of the finalized fuzzy rules bases for knowledge extraction about the relationships between physical setup values and error levels of the probes. The number of fuzzy sets on each premise leads to the number of physical setups needed to get satisfactory error profiles, whereas the fuzzy rules base adds to the knowledge linking the design experiment parameters to the pretravel error of CMM machines. Satisfactory fuzzy logic equivalents of the 3-D error profiles were obtained for both TP6 and TP200 with root mean squsre errors ranging from 0.00 mm to a maximum of 0.58 mm.
机译:决定坐标测量机(CMM)精度的基本要素之一是探针,它可以在测量体积内定位测量点。本文提出了基于遗传知识的基于模糊知识的一阶段和两阶段接触触发探针的三维(3-D)探测精度模型。模糊模型是使用作者开发的专用遗传算法自动生成的。该算法使用混合编码,规则库使用二进制编码,数据库使用实数编码。这种混合编码与一组专门的再现运算符一起使用,在这种情况下被证明是一种有效的学习环境。使用用于探针测试的特殊设置进行了被测对象坐标的数据收集。作者使用了一种新颖的方法,该方法将低力的高分辨率位移传感器应用于CMM测量之外的3-D空间中的探头误差检查。针对一阶段(TP6)和两阶段(TP200)类型的探针构建遗传生成的模糊模型。首先,通过分析模糊规则对TP6精度的影响来定义最佳设置数量。然后,一旦获得设置的数量,就会为TP6和TP200触发探针生成接近最佳的模糊知识库,然后分析最终的模糊规则库,以提取有关探针的物理设置值和错误级别之间关系的知识。 。每个前提下模糊集的数量导致获得令人满意的误差轮廓所需的物理设置的数量,而模糊规则库增加了将设计实验参数与CMM机器的预行程误差联系起来的知识。对于TP6和TP200,均获得了3-D误差曲线的令人满意的模糊逻辑等效项,其均方根误差在0.00毫米至最大0.58毫米之间。

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