Abstract
Aiming at the problems in the current surface roughness prediction methods that interface with the normal machining of machine tools, poor real-time performance, inconvenient sensor installation and high cost, a multi-sensor surface roughness prediction method based on digital twin was proposed. Firstly, the digital twin model of intelligent workshop was established as the only data source for workshop monitoring and surface roughness prediction; secondly, vibration signal was preprocessed and combined with power, energy consumption and cutting parameters to construct joint multi feature vector, and feature fusion was performed by principal component analysis; finally, support vector machine was used to predict surface roughness. The results showed that the average relative error was 4.00% and the maximum error was 0.07 μm, which verified the effectiveness of the method.
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Acknowledgements
The authors would like to express appreciation to mentors in Shanghai University for their valuable comments and other helps. Thanks for the pillar program supported by Shanghai Science and Technology Committee of China (No. 19511105200).
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Zhang, X., Liu, L., Wu, F., Wan, X. (2021). Digital Twin-Driven Surface Roughness Prediction Based on Multi-sensor Fusion. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation X. IWAMA 2020. Lecture Notes in Electrical Engineering, vol 737. Springer, Singapore. https://doi.org/10.1007/978-981-33-6318-2_29
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DOI: https://doi.org/10.1007/978-981-33-6318-2_29
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