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 3D 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 data base. 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 set-up for probe testing. The authors used a novel method that applies a low-force high-resolution displacement transducer for probe error examination in 3D space outside the CMM measurement. The genetically generated fuzzy models are constructed for both one stage (TP6) and two stage (TP200) types of probes. Firstly, 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 set-up 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, while the fuzzy rules base adds to the knowledge linking the design experiment parameters to the pre-travel error of CMM machines. Satisfactory fuzzy logic equivalents of the 3D error profiles were obtained for both TP6 and TP200 with RMS errors ranging from 0.00m to a maximum of 0.58m.
Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2011