Browse > Article

QUALITY IMPROVEMENT OF VEHICLE DRIFT USING STATISTICAL SIX SIGMA TOOLS  

PARK T. W. (Department of Mechanical Engineering, Ajou University)
SOHN H. S. (Hyundai Motor Company)
Publication Information
International Journal of Automotive Technology / v.6, no.6, 2005 , pp. 625-633 More about this Journal
Abstract
Vehicle drift was reduced using statistical six sigma tools. The study was performed through four steps: M (measure), A (analyze), I (improve), and C (control). Step M measured the main factors which were derived from a fishbone diagram. The measurement system capabilities were analyzed and improved before measurement. Step A analyzed critical problems by examining the process capability and control chart derived from the measured values. Step I analyzed the influence of the main factors on vehicle drift using DOE (design of experiment) to derive the CTQ (critical to quality). The tire conicity and toe angle difference proved to be CTQ. This information enabled the manufacturing process related with the CTQ to be improved. The respective toe angle tolerance for the adjustment process was obtained using the Monte Carlo simulation. Step C verified and controlled the improved results through hypothesis testing and Monte Carlo simulation.
Keywords
Robust engineering; Design of experiments; Drift; Monte Carlo simulation; Process capability;
Citations & Related Records

Times Cited By Web Of Science : 0  (Related Records In Web of Science)
Times Cited By SCOPUS : 0
연도 인용수 순위
  • Reference
1 Ching, C. H. and Kong, P. O. (2000). Design guidelines for robust snap fits. Int. J. Vehicle Design 23, 1/2, 56-67   DOI   ScienceOn
2 James, Paul D. (1991). Graphical displays of gauge R&R data. Annual Quality Congress, Milwaukee, WI, 45, 835-839
3 Juan, Angel A. and Vila, Alicia (2002). System reliability using Monte Carlo simulation with VBA and Excel. Quality Engineering 15, 2, 333-340   DOI   ScienceOn
4 Lamps, M. F. (1993). Improving the suspension design process by integrating multi body system analysis and design of experiments. SAE Paper No. 930264
5 Kim, S. J., Park, C. J. and Park, T. W. (1996). Suspension parameter design using the design of experiments. Trans. Korean Society of Automotive Engineers 4,1, 16-27
6 Chiang, Y. J., Shih, C. D. and Lin, C. D. (2000). Multivariable effects on sealing pressure between tires and rims. Int. J. Vehicle Design 23,1/2, 78-93   DOI   ScienceOn
7 Lindenmuth, B. E. (1974). Tire conicity and ply steer effects on vehicle performance. SAE Paper No. 740074
8 Sulieman, H. A. (2001). Profile-based approach to parametric sensitivity analysis of nonlinear regression models. Technometrics 43, 4, 425-433   DOI   ScienceOn
9 Hermens, M. (1997). A new use for Ishikawa diagrams. Quality Progress 30, 6, 81-83
10 Mark, J. Kiemele, Stephen, R. Schmidt and Ronald, J. Berdine (1997). Basic statistics-tools for continuous improvement. 9-37. Air Academy Press, Colorado Springs, Colorado, USA
11 Kim, H. S., Kim, C. B. and Yim, H. J. (2003). Quality improvement for brake judder using design for six sigma with response surface method and sigma based robust design. Int. J. Automotive Technology 4, 4, 193- 201
12 Cho, B. R. (2002). Optimum process target for two quality characteristics using regression analysis. Quality Engineering 15, 1, 37-47   DOI   ScienceOn
13 Hamada, M. (2001). Coupling Bayesian inference and Monte Carlo methods in error propagation. Quality Engineering 14, 2, 293-299   DOI   ScienceOn
14 Skrabec, Quentin Jr. (1991). Using the Ishikawa process classification diagram for improved process control. Quality Engineering 3, 4, 517-528   DOI   ScienceOn
15 Hsieh, Ghing-Shieh (2001). Analysis of ortho-gonal array experiments using the multivariate orthogonal regression method. Quality Engineering 13, 3, 449-455   DOI   ScienceOn