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Development of Statistical Models for Predicting Automobile Seat Fit of Drivers

  • Baekhee Lee
  • Kihyo Jung
  • Jangwoon ParkEmail author
Conference paper
  • 26 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1212)

Abstract

The present study is intended to develop statistical models for predicting automobile seat fit based on the relationships between seat dimensions and subjective seat fit. The evaluations of the subjective seat fit for 43 different driver seats and the seat dimensions at six cross-sectional planes (three for the seatback and the other three for the cushion) were measured and evaluated by eight seat-engineers. The best subset logistic regression analyses were conducted to quantify the relationships between the measured seat dimensions and evaluated subjective seat fit at each of the cross-sectional planes. As a result, significant seat dimensions, such as insert width or bolster height, on the subjective seat fit were identified. The developed logistic models show 90% overall classification accuracy at each section with 80% accuracy with five-fold cross-validation. The developed models would be particularly useful to support seat engineers by providing recommended seat dimensions, which could increase seat fit. In addition, the model is useful to reduce development costs for an automobile seat and increase work efficiency in the digital evaluation process of an automobile seat.

Keywords

Seat fit Seat dimension Automobile seat Logistic regression analysis 

Notes

Acknowledgments

The present study was supported by Hyundai Motor Company.

References

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Body Test Team 3Hyundai Motor CompanySuwonSouth Korea
  2. 2.School of Industrial EngineeringUniversity of UlsanUlsanSouth Korea
  3. 3.Department of EngineeringTexas A&M University-Corpus ChristiCorpus ChristiUSA

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