International Journal of Automotive Technology

The Korean Society of Automotive Engineers (한국자동차공학회)
권호 표지

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)
  • Published : 2005.12.01
Download PDF
⟨ Previous Next ⟩

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

References

  1. 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 https://doi.org/10.1504/IJVD.2000.001884
  2. Ching, C. H. and Kong, P. O. (2000). Design guidelines for robust snap fits. Int. J. Vehicle Design 23, 1/2, 56-67 https://doi.org/10.1504/IJVD.2000.001882
  3. Cho, B. R. (2002). Optimum process target for two quality characteristics using regression analysis. Quality Engineering 15, 1, 37-47 https://doi.org/10.1081/QEN-120006709
  4. Hamada, M. (2001). Coupling Bayesian inference and Monte Carlo methods in error propagation. Quality Engineering 14, 2, 293-299 https://doi.org/10.1081/QEN-100108686
  5. Hermens, M. (1997). A new use for Ishikawa diagrams. Quality Progress 30, 6, 81-83
  6. Hsieh, Ghing-Shieh (2001). Analysis of ortho-gonal array experiments using the multivariate orthogonal regression method. Quality Engineering 13, 3, 449-455 https://doi.org/10.1080/08982110108918673
  7. James, Paul D. (1991). Graphical displays of gauge R&R data. Annual Quality Congress, Milwaukee, WI, 45, 835-839
  8. Juan, Angel A. and Vila, Alicia (2002). System reliability using Monte Carlo simulation with VBA and Excel. Quality Engineering 15, 2, 333-340 https://doi.org/10.1081/QEN-120015865
  9. 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
  10. 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
  11. Lamps, M. F. (1993). Improving the suspension design process by integrating multi body system analysis and design of experiments. SAE Paper No. 930264
  12. Lindenmuth, B. E. (1974). Tire conicity and ply steer effects on vehicle performance. SAE Paper No. 740074
  13. 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
  14. Skrabec, Quentin Jr. (1991). Using the Ishikawa process classification diagram for improved process control. Quality Engineering 3, 4, 517-528 https://doi.org/10.1080/08982119108918880
  15. Sulieman, H. A. (2001). Profile-based approach to parametric sensitivity analysis of nonlinear regression models. Technometrics 43, 4, 425-433 https://doi.org/10.1198/00401700152672519