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.

Experiments of quenching were made with cylindrical specimens of carbon steel S45C of diameters from 12 to 30mm were performed. The specimens were heated by electric furnace and quenched by immersion method. In order to analyze the temperature profile(cooling curves) of carbon steel including the latent heat of phase transformation, nonlinear heat conduction problem was calculated by the numerical method of inverse heat conduction problem using the apparent heat capacity method. The difference between the calculated and the experimented cooling curves was caused by the latent heat of phase transformation, and the effects of the latent heat were especially manifest at the cooling curves of center of specimens. The temperature and the quantity of the latent heat of phase transformation depend on the cooling speed at A sub(1) transformation point, and the region for cooling speed to become zero was caused by the latent heat of phase transformation.