A sub-space artificial neural network for mold cooling in injection molding

作者：

Highlights：

• Cavity Temperature Profile (CTP) in injection molding is investigated.

• A combined Artificial Neural Network (ANN) and State Space Model (SSM) is formulated.

• ANNs are used to estimate governing SSM parameters on-line.

• The model predicts the CTP during the injection cycle based on a number of previous cycles.

摘要

•Cavity Temperature Profile (CTP) in injection molding is investigated.•A combined Artificial Neural Network (ANN) and State Space Model (SSM) is formulated.•ANNs are used to estimate governing SSM parameters on-line.•The model predicts the CTP during the injection cycle based on a number of previous cycles.

论文关键词：Intelligent system identification,Mold cooling,Artificial neural networks,Subspace modelling

论文评审过程：Received 5 July 2016, Revised 26 January 2017, Accepted 3 March 2017, Available online 6 March 2017, Version of Record 19 March 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.03.013