Affiliations: [a] Department of Mechanical Engineering, Faculty of Mechanical Engineering, Yildiz Technical University, Besiktas Campus, Besiktas, Istanbul, Turkey | [b] Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, Esenler, Istanbul, Turkey | [c] Department for Interchangeable Manufacturing and Industrial Metrology, Institute for Production Engineering and Laser Technology, Vienna University of Technology, Austria
Correspondence:
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Corresponding author. Asli G. Bulutsuz, Department of Mechanical Engineering, Faculty of Mechanical Engineering, Yildiz Technical University, 34349, Besiktas Campus, Besiktas, Istanbul, Turkey. Tel.: +90 212 383 29 59; Fax: +90 212 383 30 24; E-mails: [email protected]; [email protected].
Abstract: 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. A number 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. A Mamdani-type fuzzy inference system was 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.
