Establishment of vibration wear model for turning large-pitch thread tools and its wear suppression method

Abstract

Large-pitch thread parts are the key components of heavy-duty equipment and their function is the transmission of torque and accuracy, resulting in high precision and reliability requirements for their threaded faces. Since the vibration and tool wear are related to dynamics and tribology, respectively, directly establishing the mathematical relationship between them is complicated. Considering that the cutting force is not only the result of vibration effect but also the direct cause of tool wear, the relationship between the two is constructed in relation to the instantaneous cutting force in the process of machining. Firstly, the instantaneous cutting force models for different cutting speeds and axial feeds under vibration are constructed and verified. Secondly, the mathematical models of cutting force and rear face wear prediction are established based on the BP neural network method. Finally, based on these models, the vibration wear model of the large-pitch thread tool is obtained iteratively to describe the relationship between vibration and wear in the time domain, and the model is verified by experiments. The error in the model is within 20%. At the same time, based on the genetic algorithm and multi-objective optimization model, the optimal values of cutting parameters that minimize the impact of cutting vibration on tool wear are obtained, and a method of reducing the vibration to improve tool wear is proposed.