Event-driven Online Optimization of Cutting Parameters for Assembly Interfaces of Large-scale Components Considering Machining Tasks and Monitoring States

doi:10.3901/JME.2023.24.018

Abstract

Abstract: Aiming at the problems that the monitoring states such as tool wear and surface quality in the machining of large-scale components assembly interface are weakly related to the machining tasks, the difference between actual and theoretical values of tool wear is not considered in the optimization of cutting parameters, and the strong dependence on worker’s experience, etc. an event-driven online optimization method for cutting parameters is proposed that considers machining tasks and monitoring states. Firstly, an event driven online optimization framework of cutting parameters is established, and the event processing technology is used to monitor the machining process of the assembly interface to ensure that the machining tasks at each stage are correct, and then through the event-driven monitoring and optimization method. Secondly, under this framework, the abnormal monitoring events (such as tool life alarm, surface roughness alarm, etc.) are processed, and the hybrid genetic particle swarm optimization (GA-PSO) algorithm is triggered to realize the online optimization of cutting parameters. Finally, the effectiveness of the proposed method is verified by the machining experimental data and system of an aircraft vertical tail assembly interface sample. The results show that this method can effectively ensure the machining quality and efficiency of the assembly interface of large-scale components, and provides a theoretical basis for the monitoring of machining process and the online adaptive optimization of cutting parameters.

Key words: assembly interfaces, event-driven, machining process monitoring, online optimization of cutting parameters

CLC Number:

TP277

WANG Yahui, ZHENG Lianyu, FAN Wei, ZHAO Xiong, ZHANG Yuehong. Event-driven Online Optimization of Cutting Parameters for Assembly Interfaces of Large-scale Components Considering Machining Tasks and Monitoring States[J]. Journal of Mechanical Engineering, 2023, 59(24): 18-33.

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