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머신러닝 알고리듬을 적용한 차량 프런트도어의 재활용 적합성 정량적 평가기법 연구

원문정보

Evaluation of the Suitability of Recycling Process for Vehicle Front Door Using Symbolic Chart Method and Machine-Learning Algorithm

송준혁, 박나라, 양성모, 문상돈

피인용수 : 0(자료제공 : 네이버학술정보)

초록

영어

In this research, we evaluate on the disassemblability of recycling process for vehicle front door using the symbolic chart method and machine-learning algorithm. It is applied to the front door of 1600cc class vehicle, and then the conventional steel door and CFRP door were compared. Based on the principle symbolic chart method, the number of processes can be different according to decomposer proficiency of suitability of recycling process, so the evaluation method is required to supply this issue. The machine learning algorithm, and artificial intelligence method were applied and the applicable tools for each experiment were used to compensate the variations in the number of processes according to different proficiencies. Because CFRP front door has integrated components compare to steel door, so its disassemblability processes were decreased to 80 from 103 of the conventional steel door’s. It can be confirmed that the disassemblability was increased from the suitability of recycling equation. In case of the steel, disassemblability was approximately 60.6, in case of the CFRP is approximately 72 for car front door. Therefore, it can be concluded that the disassemblability of CFRP was better in the evaluation of suitability of recycling.

목차

ABSTRACT
1. 서론
2. 분해성 분석에 의한 재활용 적합성 평가
3. 자동차의 프런트 도어에 대한 분해성 평가
4. 결론
References

저자정보

  • 송준혁 Joonhyuk Song. Korea Institute of Carbon Convergence Technology
  • 박나라 Nara Park. Korea Institute of Carbon Convergence Technology
  • 양성모 Sungmo Yang. Professor, Division of Mechanical System Engineering, Chonbuk National University
  • 문상돈 Sangdon Mun. Member, Professor, Division of Mechanical Design Engineering, Chonbuk National University

참고문헌

자료제공 : 네이버학술정보

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