Automotive door closing effort uncertainty analysis based on Monte Carlo simulation method

被引:0
|
作者
Pereira, F. D.
de Souza, G. F. M.
机构
来源
SAFETY, RELIABILITY AND RISK ANALYSIS: BEYOND THE HORIZON | 2014年
关键词
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Among quality issues, door closing effort is a vehicle characteristic that strongly affects the customer first opinion about vehicle design. The door closing effort is affected by uncertainties in materials and manufacturing processes. Present paper presents a reliability-based method to evaluate the uncertainties associated to door closing effort due to manufacturing. Reliability analysis is used to quantify the probability that door closing effort is greater than a target value associated to variations in door gap which is strongly influenced by the upper and lower hinges position. Monte Carlo Simulation is used to define door closing effort variability due to variation of hinges position. The uncertainty of this position is modeled by a probability distribution defined based on data collected from the assembly process. Door effort variability is defined by a probability density function. Simulated distribution is compared to experimentally based door effort analysis showing a very good agreement between them.
引用
收藏
页码:3237 / 3242
页数:6
相关论文
共 50 条
  • [1] Prediction of Automotive Side Swing Door Closing Effort
    Li, Jing
    Mourelatos, Zissimos P.
    Schwarze, Frederick G.
    Rozenbaum, Joseph V.
    SAE INTERNATIONAL JOURNAL OF PASSENGER CARS-MECHANICAL SYSTEMS, 2009, 2 (01): : 271 - 284
  • [2] Uncertainty estimation and Monte Carlo simulation method
    Papadopoulos, CE
    Yeung, H
    FLOW MEASUREMENT AND INSTRUMENTATION, 2001, 12 (04) : 291 - 298
  • [3] Monte-Carlo Simulation Based on FTA in Reliability Analysis of Door System
    Zhou Liming
    Cai Guoqiang
    Yang Jianwei
    Jia Limin
    2010 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2010), VOL 5, 2010, : 713 - 717
  • [4] Analysis of uncertainty in harmonic measurement based on Monte Carlo method
    Huang, De-Hua
    Zhang, Lu-Liang
    Zeng, Jiang
    Sun, Wei-Wei
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2012, 40 (20): : 62 - 67
  • [5] MEASUREMENT UNCERTAINTY SIMULATION BY MONTE CARLO METHOD IN NANOMETROLOGY
    Sramek, Jan
    Jankovych, Robert
    MM SCIENCE JOURNAL, 2019, 2019 : 2998 - 3004
  • [6] Method of Monte Carlo simulation for the analysis of uncertainty for ultrasonic time-of-flight
    Santos, F.
    Villanueva, J.
    Gouveia, R.
    Silva, J.
    2017 JOINT IMEKO TC1-TC7-TC13 SYMPOSIUM: MEASUREMENT SCIENCE CHALLENGES IN NATURAL AND SOCIAL SCIENCES, 2018, 1044
  • [7] Topological analysis in Monte Carlo simulation for uncertainty propagation
    Pakyuz-Charrier, Evren
    Jessell, Mark
    Giraud, Jeremie
    Lindsay, Mark
    Ogarko, Vitaliy
    SOLID EARTH, 2019, 10 (05) : 1663 - 1684
  • [8] A proposal on accuracy estimation method for the sampling-based uncertainty analysis with Monte Carlo simulation technique
    Kim, Song Hyun
    Song, Myung Sub
    Sun, Gwang Min
    Shin, Chang Ho
    JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY, 2016, 53 (02) : 295 - 301
  • [9] Monte Carlo uncertainty analysis of an ANN-based spectral analysis method
    Salinas, Jose Ramon
    Garcia-Lagos, Francisco
    Diaz de Aguilar, Javier
    Joya, Gonzalo
    Sandoval, Francisco
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (02): : 351 - 368
  • [10] Uncertainty Analysis of ANN Based Spectral Analysis Using Monte Carlo Method
    Ramon Salinas, Jose
    Garcia-Lagos, Francisco
    Diaz de Aguilar, Javier
    Joya, Gonzalo
    Sandoval, Francisco
    ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT I, 2017, 10305 : 269 - 280