Development of Artificial Neural Network System to Recommend Process Conditions of Injection Molding for Various Geometries

This study combines an artificial neural network (ANN) and a random search to develop a system to recommend process conditions for injection molding. Both simulation and experimental results are collected using a mixed sampling method that combines Taguchi and random sampling. The dataset consists of 3600 simulations and 476 experiments from 36 different molds. Each datum has five process and 15 geometry features as input and one weight feature as output. Hyper-parameter tuning is conducted to find the optimal ANN model. Then, transfer learning is introduced, which allows the use of simultaneous experimental and simulation data to reduce the error. The final prediction model has a root mean-square error of 0.846. To develop a recommender system, random search is conducted using the trained ANN forward model. As a result, the weight-prediction model based on simulated data has a relative error (RE) of 0.73%, and the weight prediction using the transfer model has an RE of 0.662%. A user interface system is also developed, which can be used directly with the injection-molding machine. This method enables the setting of process conditions that yield parts having weights close to the target, by considering only the geometry and target weight.