Geometry and cooling effects on an induction self-heating insert for rapid mold temperature control: Experimental, Multiphysics, and artificial neural network-based analysis
- 1Faculty of Mechanical Engineering, HCM City University of Technology and Engineering, Ho Chi Minh City, Vietnam
Abstract
Control of mold-cavity surface temperature strongly affects filling, premature frozen-layer formation, and cycle time in thin-wall injection molding. This study evaluates an induction self-heating insert with a thin heating zone directly coupled to a cooling layer using experiments, a sequentially coupled electromagnetic-thermal model, and an Artificial Neural Network (ANN). The design space contains 25 Taguchi L25 geometries defined by T, W, and L; X and Y coordinates are added so that the ANN predicts the surface temperature field. Three cooling states are considered: No Water (NW), Stagnant Water (SW), and Flowing Water (FW). Experimental ANN targets are the mean of five repeated tests. The simulation reproduced the location of the hot zone; among four cases with reported quantitative differences, three differed by no more than about 10 °C and one showed a peak difference of about 20 °C. To prevent data leakage, all 846 points from each geometry were kept together during geometry-wise validation. The resulting R² values were 0.746, 0.653, and 0.493 for NW, SW, and FW, with Root Mean Square Error (RMSE) values of 17.03, 12.97, and 12.62 °C, respectively. The effects of T, W, and L depended on the cooling state and were not consistently monotonic. A proof-of-concept molding test showed complete filling of a Chip 2 part using Low-Density Polyethylene (LDPE); the mean spacing was 28.6 µm for a nominal 30 µm, and the mean height was 106.6 µm for a nominal 100 µm. These results show that combining insert geometry, cooling-water state, and a surrogate model can support local thermal design, delay frozen-layer formation, and improve filling in injection molding.
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BibTeX
@article{do2026geometry,
title = {{Geometry and cooling effects on an induction self-heating insert for rapid mold temperature control: Experimental, Multiphysics, and artificial neural network-based analysis}},
author = {Thanh Trung Do and Tran Minh The Uyen and Pham Son Minh},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359849},
url = {https://doi.org/10.1371/journal.pone.0359849},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Geometry and cooling effects on an induction self-heating insert for rapid mold temperature control: Experimental, Multiphysics, and artificial neural network-based analysis
AU - Thanh Trung Do
AU - Tran Minh The Uyen
AU - Pham Son Minh
PY - 2026
DA - 2026/10/27
JO - PLOS One
DO - 10.1371/journal.pone.0359849
UR - https://doi.org/10.1371/journal.pone.0359849
AB - Control of mold-cavity surface temperature strongly affects filling, premature frozen-layer formation, and cycle time in thin-wall injection molding. This study evaluates an induction self-heating insert with a thin heating zone directly coupled to a cooling layer using experiments, a sequentially coupled electromagnetic-thermal model, and an Artificial Neural Network (ANN). The design space contains 25 Taguchi L25 geometries defined by T, W, and L; X and Y coordinates are added so that the ANN predicts the surface temperature field. Three cooling states are considered: No Water (NW), Stagnant Water (SW), and Flowing Water (FW). Experimental ANN targets are the mean of five repeated tests. The simulation reproduced the location of the hot zone; among four cases with reported quantitative differences, three differed by no more than about 10 °C and one showed a peak difference of about 20 °C. To prevent data leakage, all 846 points from each geometry were kept together during geometry-wise validation. The resulting R² values were 0.746, 0.653, and 0.493 for NW, SW, and FW, with Root Mean Square Error (RMSE) values of 17.03, 12.97, and 12.62 °C, respectively. The effects of T, W, and L depended on the cooling state and were not consistently monotonic. A proof-of-concept molding test showed complete filling of a Chip 2 part using Low-Density Polyethylene (LDPE); the mean spacing was 28.6 µm for a nominal 30 µm, and the mean height was 106.6 µm for a nominal 100 µm. These results show that combining insert geometry, cooling-water state, and a surrogate model can support local thermal design, delay frozen-layer formation, and improve filling in injection molding.
SN - 1932-6203
ER -