Accurate identification of process parameters from in-mold sensing signals is essential for advancing intelligent and adaptive injection molding systems. This study proposes a data-driven inverse modeling framework that infers key process parameters directly from cavity pressure curves. A stage-wise Autoencoder (AE) is employed to perform nonlinear feature extraction by segmenting the pressure curve into filling, packing, and cooling stages, thereby capturing process-dominant dynamics. The encoded latent features are subsequently mapped to injection speed, packing pressure, nozzle temperature, and mold temperature using a multilayer perceptron (MLP). Eighty-one full-factorial parameter combinations were experimentally conducted, generating 810 molding cycles on an industrial injection molding machine. An independent validation dataset was collected from separate production runs to evaluate generalization capability. The proposed AE–MLP framework achieved high predictive accuracy on unseen data, with R² values of 0.9979 for injection speed, 0.9926 for packing pressure, 0.9571 for mold temperature, and 0.9296 for nozzle temperature. The corresponding RMSE values remained below 1.6 across all parameters. Comparative analysis against PCA–MLP and direct MLP models demonstrates that nonlinear manifold representation significantly improves inverse regression robustness, particularly for thermally coupled parameters. Furthermore, the stability of inverse modeling is analyzed from the perspective of parameter identifiability and many-to-one mapping characteristics inherent in injection molding processes. Results indicate that stage-wise nonlinear feature encoding enhances separability in the latent space and mitigates ambiguity in parameter inference. The proposed framework provides a robust and physically interpretable approach for real-time process parameter estimation, contributing to the development of adaptive and self-optimizing manufacturing systems. Infers molding parameters directly from multi-sensor pressure features. AE–MLP framework learns nonlinear relations between pressure and parameters. Parameter recommendation enables adaptive process condition adjustment. 810-cycle full-factorial experiments confirm accuracy and generalization. Supports smart manufacturing through real-time parameter tuning.
Chang et al. (Wed,) studied this question.