Earticle

초록

영어

In this paper, to improve the optical quality of aspherical plastic lenses for mobile use, the optimal molding conditions that can minimize the phase difference are derived using injection molding simulation, design of experiments, and machine learning. First, factors affecting the phase difference were derived using the design of the experiment method, and a data set was created using the derived factors, followed by the machine learning process. After predicting the model trained using the generated training data as test data and verifying it with the performance evaluation index, the model with the best predictive performance was the random forest model. Therefore, to derive the optimal molding conditions, random forests were used to predict 10,000 random pieces of data. As a result of applying the derived optimal molding conditions to the injection molding simulation, the phase difference of the lens could be reduced by 8.2%.

목차

ABSTRACT
1. 서론
2. 종속변수 및 모델
2.1 복굴절과 위상차
2.2 모바일 렌즈
2.3 해석모델
3. 실험
3.1 위상차에 유의한 인자 도출
3.2 데이터 셋 생성
3.3 기계학습 프로세스
4. 결과
5. 결론
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References

키워드

사출성형 위상차 렌즈 기계학습 인공 지능 빅 데이터 Injection molding Retardation Lens Machine learning Artificial intelligence Big data

저자

• 이용선 [ Yong-Sun Lee | Department of Optical Engineering and Metal Mold, Kongju National University ]
• 주지용 [ Ji-Yong Joo | Department of Optical Engineering and Metal Mold, Kongju National University ]
• 임세종 [ Sae-Jong Lim | Department of Optical Engineering and Metal Mold, Kongju National University ]
• 이정원 [ Jong-Won Lee | Department of Optical Engineering and Metal Mold, Kongju National University ]
• 한성열 [ Seong-Ryeol Han | Member, Associate Professor, Kongju National University ] Corresponding author

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