Earticle

초록

영어

The automotive industry is undergoing a paradigm shift due to the convergence of IT and rapid digital transformation. Various components, including embedded structures and systems with complex architectures that incorporate IC semiconductors, are being integrated and modularized. As a result, there has been a significant increase in vehicle defects, raising expectations for the quality of automotive parts. As more and more data is being accumulated, there is an active effort to go beyond traditional reliability analysis methods and apply machine learning models based on the accumulated big data. However, there are still not many cases where machine learning is used in product development to identify factors of defects in performance and durability of products and incorporate feedback into the design to improve product quality. In this paper, we applied a prediction algorithm to the defects of automotive door devices equipped with automatic responsive sensors, which are commonly installed in recent electric and hydrogen vehicles. To do so, we selected test items, built a measurement emulation system for data acquisition, and conducted comparative evaluations by applying different machine learning algorithms to the measured data. The results in terms of R2 score were as follows: Ordinary multiple regression 0.96, Ridge regression 0.95, Lasso regression 0.89, Elastic regression 0.91.

목차

Abstract
1. Introduction
2. Previous Research and principal Background
2.1 Machine learning algorithms
3. Propose method and discussion
3.1 System Design and Configuration
3.2 Machine Learning Process
3.3 Data pre-processing
3.4 Training and Validation data splitting
3.5 Model selection and Training
3.6 Prediction and Evaluation
4. Simulation
5. Conclusion
References

키워드

저자

• Yun-Seok Seo [ Ph. D. Candidate, Department of Computer Engineering, Tech University, Kor ]
• Young-Gon Kim [ Professor, Department of Computer Engineering, Tech University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380