Vibration Characteristics Research Of High Frequency Rotating Cantilever Mechanism Using Symbolic Regression

High frequency rotary motion of the cantilever mechanism has been widely used on the market at present,the structure movement stability of the cantilever structure under the high frequency of rotation is very important,but little research on the vibration analysis of the cantilever rotational structure at home and abroad At the same time in industrial production field,the research on vibration characteristics of cantilever rotary structure under high frequency needs to increase investment in time and energy of scientific research workers.Moreover,there is no research on the swing arm motion with the component of quick stop reversing motion at home and abroad.And it is urgent to adapt to the high frequency rotary motion of the cantilever mechanism controlled by big data.In this paper,the vibration stability of high frequency cantilever mechanism is studied by means of modal analysis.In operating modal analysis,the excitation force needs to meet the white noise hypothesis,so it cannot be used for the cantilever mechanism studied in this paper.In this paper,the effect of the periodic high-frequency emergency-stop-reversing excitation of the cantilever under the condition of operation and under the condition of static percussion incentives are similar,and the dynamic shape of structure is different from the static one,so this paper used a new experimental modal analysis method for modal parameter identification under high frequency work.It is considered that the hypothesis that periodic high-frequency emergency-stop-reversing excitation is seemed as the impulse excitation,and the response is the superposition of the single impulse excitation response with sufficient intervals.But the problem is that the enough number of single pulse is difficult to determine,in order to solve the problem of the dynamic self-adapting obtaining modal parameters,we put forward a method of using symbolic regression.Then,the simulation test and cantilever work-piece knock test are designed to verify the modal parameters extracted by symbolic regression.The results are compared with the modal parameters analysis results of the LMS experimental platform to verify the correctness of the method.Finally,the experiment is designed and the strain signal of the chip sorter of the high frequency lower cantilever mechanism is collected at the same time.Modal parameter identification results under high frequency are selected as sample and considering the recognition result is volatility,machine learning algorithms Gaussian process regression model is used.The variation law of running vibration characteristics of rotary pendulum arm under high frequency is established for prediction,and this model will seem inherent frequency as random variable which obeys Gaussian distribution.Moreover the model can be very good deal with volatility,and the natural frequency of the predicted results can beused as a cantilever mechanism chip sorting machine for high frequency rotating under the condition of modal parameter identification and prediction under the high frequency vibration characteristics.