• 한국정밀공학회지
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• 제24권2호
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• pp.110-117
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• 2007

In this paper, probabilistic distribution of chassis component fatigue life is determined statistically by applying the design of experiments and the Pearson system. To construct p - ${\varepsilon}$ - N curve, the case that fatigue data are random variables is attempted. Probabilistic density function (p.d.f) for fatigue life is obtained by the design of experiment and using this p.d.f fatigue reliability, any aimed fatigue life can be calculated. Lower control arm and rear torsion bar of chassis components are selected as examples for analysis. Component load histories which are obtained by multi-body dynamic simulation for Belsian load history are used. Finite element analysis is performed by using commercial software MSC Nastran and fatigue analysis is performed by using FE Fatigue. When strain-life curve itself is random variable, the probability density function of fatigue life has very little difference from log-normal distribution. And the cases of fatigue data are random variables, probability density functions are approximated to Beta distribution. Each p.d.f is verified by Monte-Carlo simulation.

• 한국생산제조학회지
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• 제23권4호
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• pp.337-344
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• 2014

While implicit integration methods such as Newton's method have excellent stability for the analysis of stiff and constrained mechanical systems, they have the drawback that the evaluation and LU-factorization of the system Jacobian matrix required at every time step are time-consuming. This paper proposes a Jacobian update-free Newton's method in order to overcome these defects. Because the motions of all bodies in a vehicle model are limited with respect to the chassis body, the equations are formulated with respect to the moving chassis-body reference frame instead of the fixed inertial reference frame. This makes the system Jacobian remain nearly constant, and thus allows the Newton's method to be free from the Jacobian update. Consequently, the proposed method significantly decreases the computational cost of the vehicle dynamic simulation. This paper provides detailed generalized formulation procedures for the equations of motion, constraint equations, and generalized forces of the proposed method.

• 대한기계학회논문집A
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• 제40권12호
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• pp.997-1003
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• 2016

본 논문은 자세 제어 장치와 능동 후륜 조향장치를 가지는 통합 섀시 제어에서 요 모멘트 분배를 위해 적응 알고리즘을 적용하는 방법을 제안한다. 통합 섀시 제어는 상위제어기와 하위제어기로 구성된다. 상위제어기에서 슬라이딩 모드 제어 이론을 이용하여 차량을 안정화시키는데 필요한 제어 요 모멘트를 계산한다. 하위제어기에서는 제어 요 모멘트를 만들어 내기 위해 자세 제어 장치의 제동 압력과 능동 후륜 조향장치의 조향각을 결정하는 데에 적응 알고리즘을 적용한다. 차량 시뮬레이션 패키지인 CarSim에서 시뮬레이션을 수행하여 제안된 방법의 타당성을 검증한다.

• 제어로봇시스템학회논문지
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• 제21권7호
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• pp.595-601
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• 2015

This paper presents an unified chassis control with electronic stability control (ESC) and active front steering (AFS) under lateral force constraint on AFS. When generating the control yaw moment, an optimization problem is formulated in order to determine the tire forces, generated by ESC and AFS. With Karush-Kuhn-Tucker optimality condition, the optimum tire forces can be algebraically calculated. On low friction road, the lateral force in front wheels is easily saturation. When saturated, AFS cannot generate the required control yaw moment. To cope with this problem, new constraint on the lateral tire force is added into the original optimization problem. To check the effectiveness of the propose method, simulation is performed on the vehicle simulation package, CarSim.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• 오토저널
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• 제31권2호
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• pp.16-23
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• 2009

• 드라이브 ㆍ 컨트롤
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• 제4권1호
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• pp.9-15
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• 2007

• 오토저널
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• 제24권2호
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• pp.35-36
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• 2002

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 동계 학술대회 논문집
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• pp.3-9
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• 2000

• 한국자동차공학회논문집
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• 제17권2호
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• pp.165-170
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• 2009

Pressure ripple, vibration and noise level are measured in each parts of the power steering system. MD(Mahalanobis Distance) is calculated by using MTS(Mahalanobis Taguchi System) with measured data, and noise sensitive components are selected. The components applied detail design parameters are made and data is measured. After that MD is calculated also. Mean value and SN ratio can be obtained from the MD. Effective noise reduction technique and dominant design parameters in hydraulic power steering system are introduced.