  Injection molded part quality can be improved by precise process adjustment,
which could rely on in-situ measurements of part quality. Geometrical and
appearance quality (visually and sensory) requirements are increasing. However,
direct measurement is often not feasible industrially. Therefore, process
control must rely on a prediction of parts quality attributes. This study
compares prediction performances of diverse neural networks architectures with
"classical" regression algorithms. Dataset comes from inline industrial
measurements. Regression was performed on 97 scalar statistical features
extracted from multiple acquisitions sources: thermographic images and analog
signals. Haralick features were extracted. Convolutional Neural Networks were
trained on thermographic images and Long Short Term Memory networks were
trained on raw signals. Although the dataset was small, neural networks show
better predictions scores than other regression algorithms.
