Abstract
In batch assembly, fast and accurate assessment of MT-LA straightness error is significant important for controlling of MT-LA assembly quality. In this study, in order to construct MT-LA straightness error assessment model, a data-driven method based on the bootstrap resampling approach improved fast correlation based filter (BR-FCBF) algorithm and genetic algorithm optimized multi-class support vector machine (GA-MSVM) algorithm is proposed. Firstly, the BR-FCBF algorithm is used to select the key assembly parameters that affect the straightness error. Secondly, the GA-MSVM algorithm is applied to construct the straightness error assessment model. Finally, the assembly-related data collected on a MT-LA assembly workshop is used to verify the proposed method. The experimental results show that the constructed straightness error assessment model has shown good performance in straightness error assessment.
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This research is supported by the Key Project of Shaanxi Province (No. 2017ZDCXL-GY-01-02-02) and the National Key Research and Development Project of China (No. 2018YFB1701200).
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Hui, Y., Mei, X., Jiang, G., Zhao, F. (2019). Straightness Error Assessment Model of the Linear Axis of Machine Tool Based on Data-Driven Method. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11743. Springer, Cham. https://doi.org/10.1007/978-3-030-27538-9_47
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DOI: https://doi.org/10.1007/978-3-030-27538-9_47
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