Explainable Temporal Attribution for Time-Series-Based Quality Prediction in Injection Molding

Abstract

Time-series sensor signals collected during injection molding
contain rich information about process dynamics and final
part quality. However, quality prediction models based on
high-dimensional temporal signals are often difficult to in
terpret, and direct attribution in the original signal domain
may produce unstable explanations. This paper proposes an
explainable temporal attribution framework for time-series
based quality prediction in injection molding. The framework
first learns compact latent representations of multivariate pro
cess signals using a one-dimensional convolutional autoen
coder, and then trains a latent-space prediction model for
defect classification or dimensional quality regression. To re
cover process-level interpretability, SHapley Additive exPlana
tions (SHAP) are computed in the latent space and propagated
back to the original temporal signal domain through the de
coder Jacobian. The proposed framework is evaluated on two
injection molding datasets. The results show that the learned
latent representations preserve dominant temporal process
characteristics and provide useful information for downstream
quality prediction. The temporal attribution results reveal that
pressure-related signals and the filling-to-packing transition
are especially important for quality prediction, while phase
aware analysis indicates that packing and cooling stages play
dominant roles in sink mark prediction. Robustness analysis
further demonstrates that the decoder-based temporal attribu
tion framework provides more stable and consistent explana
tions than direct SHAP analysis in the original signal domain.
The proposed method offers an interpretable way to connect
latent predictive features with physically meaningful mold
ing process dynamics, supporting process understanding and
quality-related decision making.