Knowledge graph-based intelligent control systems for real-time machine tool optimization

The stability of computer numerical control machine tool operation directly affects production efficiency and quality. However, there are some uncertain factors concerning with environment in which the control effect of these tools is not ideal. To address these issues a knowledge graph-based real-time optimization intelligent control system for computer numerical control machine tools is used. This study uses the research results from the field of machine tools to establish a knowledge graph. Furthermore, a method based on the graph neural network is employed to relate the knowledge graph data with the functioning of the machine tools. In addition, for further details, the data is classified into different groups by using clustering algorithms resulting in corresponding decision-making outcomes. The results showed that the decision accuracy and workpiece quality qualification rate were 98.74% and 95.47%, respectively. The decision response time of the system was only 1.09. By using a knowledge graph, a real-time optimization intelligent control system for machine tools can guarantee the stability of their performance and provide precise control decision-making, enhancing their performance and thereby improving the quality of the products being produced.