사출성형공정에서 데이터의 불균형 해소를 위한 담금질모사

• 영문명: Simulated Annealing for Overcoming Data Imbalance in Mold Injection Process
• 발행기관: 한국산업경영시스템학회
• 저자명: 이동주
• 간행물 정보: 『산업경영시스템학회지』제45권 제4호, 233~239쪽, 전체 7쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2022.12.31

영문 초록

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

목차

1. 서 론

2. 오버샘플링, 복합샘플링 기법

3. 제안하는 방법

4. 실 험

5. 결론 및 연구과제

키워드

Data Imbalance Mold Injection Data Mining Simulated Annealing