Development of Artificial Neural Network System to Recommend Process Conditions of Injection Molding for Various Geometries. (23rd July 2020)

• Record Type: Journal Article
• Authors: Lee, Chihun; Na, Juwon; Park, Kyongho; Yu, Hyeonjae; Kim, Jongsun; Choi, Kwonil; Park, Dongyong; Park, Seongjin; Rho, Junsuk; Lee, Seungchul
• Is Part Of: Advanced intelligent systems. Volume 2:Number 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-23
• Subjects: injection-molding computer-aided engineering -- manufacturing process optimization -- process control -- search algorithm -- transfer learning; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.202000037
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : 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. Abstract : An artificial neural network‐based process recommender system can reduce the time and error compared with simulation‐based optimization. For this, efficient sampling‐based data acquisition is required. Hyper‐parameter tuning and transfer learning are introduced to improve the prediction model performance. As a last step, the final result is verified with experiment.