Abstract
ABSTRACT The vibrational characteristics of machine tools are influenced by the coupling effect of cutting excitation and position-dependent structural dynamics. The analysis of machine tool vibration characteristics during the machining process has high-dimensional characteristics. However, existing methods are only suitable for vibration characteristic analysis under a few typical position and excitation combinations, making it difficult to visualize and characterize high-dimensional vibration characteristics under the coupling effect of multiple factors. To solve this problem, a novel method based on knowledge graph (KG) is proposed to characterize and suppress cutting vibrations under the coupling effect of varying cutting excitation and position-dependent dynamics. To construct the KG of machine tool vibration characteristics, experimental and analytical methods in the field of machine tool vibration are used for data acquisition and knowledge extraction, respectively. Then, the knowledge of vibration characteristics is represented and stored by using the Neo4j graph database. Based on the constructed KG, the interactive analysis, querying and reasoning of vibration characteristics during the machining process are realized. Finally, a method for selecting the optimal machining positions and parameters based on KG is proposed, and the feasibility and effectiveness of our proposed method are validated by cutting experiments.
Published Version
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