Investigation of friction induced vibration in lead screw system using FE model and its experimental validation

Kim

2023

Sensors

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This study investigated the squeal mechanism induced by friction in a lead screw system. The dynamic instability in the friction noise model of the lead screw was derived through a complex eigenvalue analysis via a finite element model. A two degree of freedom model was described to analyze the closed solutions generated in the lead screw, and the friction noise sensitivity was examined. The analysis showed that the main source of friction noise in the lead screw was the bending mode pair, and friction-induced instability occurred when the ratio of the stiffness of the bending pair modes was 0.9–1. We also built an architecture to predict multiple outputs from a single model using deep neural networks and demonstrated that friction-induced instability can be predicted by deep neural networks. In particular, instability with nonlinearity was predicted very accurately by deep neural networks with a maximum absolute difference of about 0.035.

Section: Introductionmentioning

confidence: 99%

Analysis of Friction Noise Mechanism in Lead Screw System of Autonomous Vehicle Seats and Dynamic Instability Prediction Based on Deep Neural Network

Kim

2023

Sensors

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“…Squeak is a self-excited vibration caused by friction that frequently occurs in automobile brakes, artificial hip joints, and gear systems [ 8 , 10 , 11 ]. Many studies have examined the vibration instability caused by friction based on an analysis of brake squill noise.…”

Section: Introductionmentioning

confidence: 99%

“…They also analyzed the influence of squeal. Nam et al [ 10 ] investigated the vibration instability in the lead screw system experimentally and analyzed the instability mechanism using the finite element method (FEM). Ouenzerfi et al [ 11 ] examined the frictional instability occurring in an artificial hip joint and investigated the instability through a detailed FEM.…”

Section: Introductionmentioning

confidence: 99%

Classification of Chaotic Squeak and Rattle Vibrations by CNN Using Recurrence Pattern

Kang

2021

Sensors

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The chaotic squeak and rattle (S&R) vibrations in mechanical systems were classified by deep learning. The rattle, single-mode, and multi-mode squeak models were constructed to generate chaotic S&R signals. The repetition of nonlinear signals generated by them was visualized using an unthresholded recurrence plot and learned using a convolutional neural network (CNN). The results showed that even if the signal of the S&R model is chaos, it could be classified. The accuracy of the classification was verified by calculating the Lyapunov exponent of the vibration signal. The numerical experiment confirmed that the CNN classification using nonlinear vibration images as the proposed procedure has more than 90% accuracy. The chaotic status and each model can be classified into six classes.

“…In particular, mechanical vibrations can generate a chaotic phenomenon because they contain strong nonlinearity stemming from the discontinuity of friction and impact. Vibration problems occurring in general friction systems, including nonlinearity, were analyzed for stability via linearization [8][9][10]. On the other hand, after frictional vibration occurs, the nonlinear signal of the vibration increases and can become chaotic.…”

Section: Introductionmentioning

confidence: 99%

Classification of Chaotic Signals of the Recurrence Matrix Using a Convolutional Neural Network and Verification through the Lyapunov Exponent

Kang

2020

Applied Sciences

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This study classified chaotic time series data, including smooth and nonsmooth problems in a dynamic system, using a convolutional neural network (CNN) and verified it through the Lyapunov exponent. For this, the classical nonlinear differential equation by the Lorenz model was used to analyze a smooth dynamic system. The vibro-impact model was used for the nonsmooth dynamic system. Recurrence is a fundamental property of a dynamic system, and a recurrence plot is a representative method to visualize the recurrence characteristics of reconstructed phase space. Therefore, this study calculated the Lyapunov exponent by parametric analysis and visualized the corresponding recurrence matrix to show the dynamic characteristics as an image. In addition, the dynamic characteristics were classified using the proposed CNN model. The proposed CNN model determined chaos with an accuracy of more than 92%.