Abstract
The reliability of machine tools is highly influenced by the cutting state. The traditional recognition method of cutting state is emphasized on a single classifier, which has the weakness of low identification accuracy and strong randomness. This paper proposes a cutting-state identification method based on improved Dempster-Shafer (DS) evidence theory. This method is divided into multi-classifier preliminary-diagnosis layer and improved DS information-fusion layer. The wavelet packet analysis method is extracted as the input of multi-classifier (Back Propagation (BP) neural network, genetic algorithm (GA) optimized BP neural network and thinking evolution (mind evolutionary algorithms) MEA optimized BP neural network). After the preliminary judgment, the improved DS information-fusion method is integrated as the final judgment, and finally, the effectiveness and feasibility of the method are verified.
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Funding
The project is supported by Transformation Program of Scientific and Technological Achievements of HeBei Provence.” The production, integration, and application of the industrial robot” (19041827Z).
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A diagnosis method of cutting state of machine tools based on improved DS algorithm is proposed. By collecting the vibration information of the machine tools and using the DS classifier for information fusion diagnosis, the final machining state of the equipment is accurately judged. Compared with the traditional single classifier, this method greatly improved the reliability of diagnosis.
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Xu, B., Sun, Y. Cutting-state identification of machine tools based on improved Dempster-Shafer evidence theory. Int J Adv Manuf Technol 124, 4099–4106 (2023). https://doi.org/10.1007/s00170-022-09056-9
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DOI: https://doi.org/10.1007/s00170-022-09056-9