Duffing-Holmes振子在刀具磨损检测中的应用

Condition Monitor of Tool Wear Based on Duffing-Holmes

针对切削过程中刀具磨损程度难以识别的问题,文章根据混沌Duffing-Holmes振子对微弱周期信号极其敏感的特点,提出将锋利和磨损的刀具在27种工作状态下采集的54组声发射数据作为外部的微弱摄动信号,分别输入到Duffing-Holmes系统。为了解决混沌阈值求解时间的问题,研究了对分法最优算法来搜索Duffing-Holmes从周期态和混沌态的Lyapunov指数阈值,首先,以0.1步长的Lyapunov指数确定一个粗略的阈值;然后根据对分法快速收索DuffingHolmes振子混沌阈值的精确值,大大提高了阈值搜索速度,阈值大小对应了刀具磨损程度。

For the problem that cutter wear degree is difficult to identify during cutting process,in view of DuffingHolmes oscillators is sensitive to weak period signal,54 groups of acoustic emission（ AE） data from sharp tool and wear tool under 27 kinds of cutting condition were collected as external weak perturbation signal and then be inserted into Duffing-Holmes system respectively. In order to solve the problem of chaos threshold slowly,bisection algorithm to quickly research the threshold of Lyapunov exponent which transfer Duffing-Holmes from period state to chaos state was researched. First,take 0.1 as the step size to find a rough threshold based on the Lyapunov exponent. Then use Bisection algorithm to figure out the exact value of the threshold. This algorithm greatly improves the speed for searching threshold and its value corresponds to the wear degree of the tools.