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Coordinate measuring machines (CMM) have a vital and enduring role in the manufacturing process because of their easy adaptation to the systems and high measurement accuracy. Owing to the demand for high accuracy and shorter cycle times of measurement tasks, determining the measurement errors has become more important in precision engineering. Additionally, manufactured components are becoming smaller and tolerances becoming tighter, and therefore, demands for accuracy are increasing. For this reason, dynamic error modeling has become a topic of considerable importance for improving measurement accuracy, manufacturing decisions and process parameter selections.Anumber of factors such as process parameters, measurement environment, measuring object, reference element, measurement equipment and set-up affect the measurement accuracy of CMM. Considering the complicated inter-relationships among a number of system factors, artificial intelligence-based techniques have become essential tools due to their speed, robustness and non-linear characteristics when working with high-dimensional data. In this study, a fuzzy logic-based methodology was implemented as an artificial intelligence approach for determining measurement errors related to the process parameters for coordinate measuring machines.AMamdani-type fuzzy inference systemwas developed within the framework of a graphical user interface. Eight-level trapezoidal membership functions were employed for the fuzzy subsets of each model variable. The product and the centre of gravity methods were performed as the inference operator and defuzzification methods, respectively. The proposed prognostic model provided a well-suited method and produced promising results in predicting measurement errors by monitoring the process parameters such as optimum measuring point numbers, probing speed and probe radius. [ABSTRACT FROM AUTHOR]

In this study, an adaptive neuro-fuzzy inference system (ANFIS) including five process variables, such as influent chemical oxygen demand, influent flow rate, influent total Kjeldahl nitrogen, effluent volatile fatty acids and effluent bicarbonate, was described to predict the effluent chemical oxygen demand load from a full-scale expanded granular sludge bed reactor (EGSBR) treating corn processing wastewater. The proposed ANFIS model was conducted by applying hybrid learning algorithm and the model performance was tested by the means of distinct test data set randomly selected from the experimental domain. The ANFIS-based predictions were also validated using various descriptive statistical indicators, such as root mean-square error, index of agreement, the factor of two, fractional variance, proportion of systematic error, etc. The lowest root mean square error (RMSE = 0.03655) and the highest determination coefficient (R2 = 0.958) were achieved with the subtractive clustering of a first-order Sugeno type fuzzy inference system. ANFIS predicted results were compared with the outputs of multiple nonlinear regression analysis-based models derived in the scope of the present work. Statistical performance indices computed for the testing data set proved that the developed ANFIS-based model exhibited a very good precision in predicting the effluent chemical oxygen demand, load for the EGSBR system. Due to high capability of the ANFIS model in capturing the dynamic behavior and non-linear interactions, it was demonstrated that a complex system, such as anaerobic digestion, could be easily modeled. [ABSTRACT FROM AUTHOR]