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Purpose <p>Titanium alloy is a type of metal that is difficult to cut, and during turning, the signals of turning temperature, turning vibration, and turning noise are coupled with each other. Therefore, quantitative research on the coupling influence of multiple turning signals is highly important.</p> Methods <p>First, signal acquisition and analysis system are built to obtain time-domain signals of three-direction vibration acceleration (TDVA), the sound pressure of noise, and temperature through multisource sensors and other acquisition systems. Then, TDVA signal is denoised via the wavelet packet threshold denoising method, and characteristic values of TDVA are reduced to one dimension via principal component analysis. The coupling coordination model is constructed to calculate coupling coordination between turning signals. Finally, a multi-objective optimization model of minimum turning temperature, minimum turning vibration, minimum turning noise sound pressure, and maximum metal removal rate is established via the multi-objective optimization method, and the optimal parameters are obtained via the improved particle swarm algorithm.</p> Conclusions <p>The results demonstrate that (1) The degree of coupling between turning noise and turning temperature is the highest. (2) The optimal parameters are as follows: the turning speed is 38.0478 m/min, the feed speed is 79.9801 mm/min, and the turning depth is 0.25 mm.</p>

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Study on the Properties of Multi Signals Coupling Between Noise-Vibration-Temperature in TC4 Turning and Parameter Optimization Based Multi-Objective

  • 期刊论文

  • 发表日期：

    2025年7月4日

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  • Li Yang,
  • Minghui Shao,
  • Xiaofeng Ji,
  • Shuncai Li

摘要

Purpose

Titanium alloy is a type of metal that is difficult to cut, and during turning, the signals of turning temperature, turning vibration, and turning noise are coupled with each other. Therefore, quantitative research on the coupling influence of multiple turning signals is highly important.

Methods

First, signal acquisition and analysis system are built to obtain time-domain signals of three-direction vibration acceleration (TDVA), the sound pressure of noise, and temperature through multisource sensors and other acquisition systems. Then, TDVA signal is denoised via the wavelet packet threshold denoising method, and characteristic values of TDVA are reduced to one dimension via principal component analysis. The coupling coordination model is constructed to calculate coupling coordination between turning signals. Finally, a multi-objective optimization model of minimum turning temperature, minimum turning vibration, minimum turning noise sound pressure, and maximum metal removal rate is established via the multi-objective optimization method, and the optimal parameters are obtained via the improved particle swarm algorithm.

Conclusions

The results demonstrate that (1) The degree of coupling between turning noise and turning temperature is the highest. (2) The optimal parameters are as follows: the turning speed is 38.0478 m/min, the feed speed is 79.9801 mm/min, and the turning depth is 0.25 mm.