Earticle

초록

영어

For a plastic diffusion lens to uniformly diffuse light, it is important to minimize deformation that may occur during injection molding and to minimize deformation. It is essential to control the injection molding condition precisely. In addition, as the number of meshes increases, there is a limitation in that the time required for analysis increases. Therefore, We applied machine learning algorithms for faster and more precise control of molding conditions. This study attempts to predict the deformation of a plastic diffusion lens using the Decision Tree regression algorithm. As the variables of injection molding, melt temperature, packing pressure, packing time, and ram speed were set as variables, and the dependent variable was set as the deformation value. A total of 256 injection molding analyses were conducted. We evaluated the prediction model's performance after learning the Decision Tree regression model based on the result data of 256 injection molding analyses. In addition, We confirmed the prediction model's reliability by comparing the injection molding analysis results.

목차

ABSTRACT
1. 서론
2. 본론
2.1 사출성형해석을 통한 데이터 세트 생성
2.2 머신러닝 모델 학습
3. 결과
3.1 예측 모델 평가
3.2 신뢰성 평가
4. 결론
References

키워드

저자

• 유민지 [ Min-Ji Yoo | Department of Optical Engineering and Metal Mold, Kongju National University ]
• 김범수 [ Bum-Soo Kim | Department of Optical Engineering and Metal Mold, Kongju National University ]
• 김승수 [ Seung-soo Kim | Department of Optical Engineering and Metal Mold, Kongju National University ]
• 한석기 [ Seok-gi Han | Department of Optical Engineering and Metal Mold, Kongju National University ]
• 한성열 [ Seong-ryeol Han | Member, Associate Professor, Kongju National University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620