Digital Twin Control Method of Machine Tool Feed Rate Based on Fuzzy Neural Network - YIN Jia; BAI Lele; TIAN Hui; WU Shijie; Hou Guijin; TANG Yuyang; LIU Hongguang; ZHANG Jun (Journal of Harbin University of Science and Technology)
The role of the digital twin in the cutting process is to connect machine tools with the developed digital monitoring system so that the machine tools can realize condition monitoring and adaptive optimization in the process of complex parts machining, and improve the machining accuracy and efficiency of parts. In the current milling process of aviation parts, considering many factors such as deformation, roughness, and tool wear, process engineers always set conservative parameters based on experience, resulting in low machining efficiency, and an inability to adaptively adjust the cutting parameters according to the cutting trajectory and machine load. This study proposes a digital twin framework for adaptive control of milling parameters under constant load constraints. First, a dual-input and single-output fuzzy neural network is constructed to realize the calculation of the feed parameter that needs to be adjusted. The fuzzy neural network is trained based on the machining data, and the corresponding fuzzy control rules and membership functions are obtained. The control rules are modified according to the actual machining conditions. Then, an online control system for feed parameters is built based on the Siemens 840Dsl system. Finally, the effectiveness of the developed digital twin system for adaptive control of milling parameters was verified under three complex operation conditions, including variable cutting depth, curve, and slot cutting experiments.