Large Thread Whirlwind Milling Process Parameters Optimization And On-line Monitoring The Tool Wear

Large-size precision thread is the key functional part, which is able to change rotary motion to lineal motion for kinds of heavy-duty Machine Tools. High-speed or ultra-high-speed grinding is mainly used in traditional precision thread processing which is low productivity, and unstable machining accuracy. Whirling is a new cutting way in recent years, it has become the mainstream way of cutting for efficient, precise, clean and is widely applied to large-scale precision threaded and screw machining. The purpose of this paper was to monitor the tool wear of Whirlwind. Meanwhile, we combine theoretical and experimental method to study the wear and breakage of tool, and optimize the technological parameters.First of all, the failure forms and mechanism of PCBN cutters were analyzed through the experimental study, metallographic analysis and SEM electron microscope technology. Besides, the wear and breakage mechanism of PCBN cutters was studied with statistical analysis method. The relationships of the PCBN tool life with the cutting speed, depth of cut and cooling method were analyzed through single factor test, which laid the foundation for further analysis of tool wear and cutting optimization of technological parameters.Then, the optimization method of whirlwind milling process parameter of the large screw was studied. We focused on the particle swarm algorithm for it has the advantage of fast convergence. For the shortcomings of the algorithm, the increasing of the diversity of the population of particle swarm algorithm was proposed, and by comparison with the MATLAB genetic algorithm optimization toolbox, orthogonal experiment analysis of the results obtained, it proved that the improved particle swarm algorithm can be used to optimize the cutting process parameter optimization problem under the constraint of a variety of processing conditions. Meanwhile, the results of the optimization of process parameters were of great significance to improve the production efficiency, reduce the rate of tool wear and extend tool life.Finally, the trend of tool wear was obtained by the analysis of time domain and frequency domain of vibration signal in different wear conditions. The different frequency domain interval wavelet energy trend can be seen by wavelet packet analysis of vibration signal, combined with the analysis of the color and morphology of the scene processing chip,we can provide technical support and theoretical guidance for the online monitoring of the tool wear.