Reuse

Linguistics

Process (computing)

Engineering

Philosophy

Transfer of learning

Materials science

Asymptote

Operating system

Set (abstract data type)

Computer science

Molding (decorative)

Extractor

Waste management

Ranking (information retrieval)

Mathematics

Hyperparameter

Feature (linguistics)

Process engineering

Artificial intelligence

Geometry

Composite material

Programming language

Artificial neural network

Machine learning

Journal of Manufacturing Processes

Finding appropriate machine setting parameters in injection molding remains a difficult task due to the highly nonlinear process behavior. Artificial neural networks are a well-suited machine learning method for modelling injection molding processes, however, it is costly and therefore industrially unattractive to generate a sufficient amount of process samples for model training. Therefore, transfer learning is proposed as an approach to reuse already collected data from different processes to supplement a small training data set. Process simulations for the same part and 60 different materials of 6 different polymer classes are generated by design of experiments. After feature selection and hyperparameter optimization, finetuning as transfer learning technique is proposed to adapt from one or more polymer classes to an unknown one. The results illustrate a higher model quality for small datasets and selective higher asymptotes for the transfer learning approach in comparison with the base approach.

Transfer learning with artificial neural networks between injection molding processes and different polymer materials