Inverse modeling of process parameters from data to predict the cooling behavior in injection molding

Journal of Manufacturing Processes, vol. 141, pp. 760–772

DOI: 10.1016/j.jmapro.2025.02.057 OpenAlex

Authors 3

Sven Robert Raisch

Robert Bosch (Germany)

Affiliation as printed

Central Research, Robert Bosch GmbH, Robert-Bosch-Campus 1, Renningen, 71272, Baden-Wuertemberg, Germany
Christian Hopmann Aachen

RWTH Aachen University

Affiliation as printed

IKV, RWTH, Seffenter Weg 201, Aachen, 52074, Nordrhein-Westfalen, Germany
Mauritius Schmitz Aachen

RWTH Aachen University

Affiliation as printed

IKV, RWTH, Seffenter Weg 201, Aachen, 52074, Nordrhein-Westfalen, Germany

Cited by 8 stored of 8

Inverse Design of TMD for Furnace-Front Operation Robots via Artificial Neural Network
2026 Acta Mechanica Solida Sinica article Engineering Vibration Control and Rheological Fluids

0

citations
Multi-objective design and evaluation of grinding wheels for ultrasonic vibration-assisted grinding of fiber-reinforced titanium matrix composites
2026 Journal of Manufacturing Processes article Engineering Advanced machining processes and optimization

4

citations
A gradient descent method for inverse learning of a sintering model with specific physical parameter denormalization
2026 Journal of the European Ceramic Society article Materials Science Machine Learning in Materials Science Open access

2

citations
Multi-objective design and evaluation of grinding wheels for ultrasonic vibration-assisted grinding of fiber-reinforced titanium matrix composites
2026 SSRN Electronic Journal preprint Engineering Advanced machining processes and optimization Open access

0

citations
Study on fracture mechanism of Ti6Al4V high-speed cropping process and prediction of fracture surface quality based on physics-informed neural networks
2025 Theoretical and Applied Fracture Mechanics article Engineering Advanced machining processes and optimization

2

citations
Inverse solution of process parameters in gear grinding using hierarchical bayesian physics informed neural network (HBPINN)
2025 Scientific Reports article Physics and Astronomy Model Reduction and Neural Networks Open access

2

citations
A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites
2025 Advanced Manufacturing Polymer & Composites Science article Materials Science Machine Learning in Materials Science Open access

12

citations
Segmentation and Modeling of Injection Molding Production Processes Driven by Input Electrical Signals
2025 Journal of Applied Polymer Science article Engineering Manufacturing Process and Optimization

2

citations

8 results

No patents citing this paper on Lens.org (checked 2026-10-06).

References 41

A Survey on Transfer Learning
2009 IEEE Transactions on Knowledge and Data Engineering article Computer Science Domain Adaptation and Few-Shot Learning

23,913

citations
A survey of transfer learning
2016 Journal Of Big Data article Computer Science Domain Adaptation and Few-Shot Learning Open access

6,368

citations
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
2022 Journal of Scientific Computing article Physics and Astronomy Model Reduction and Neural Networks Open access

2,768

citations
Physics-informed neural networks (PINNs) for fluid mechanics: a review
2021 Acta Mechanica Sinica article Physics and Astronomy Model Reduction and Neural Networks Open access

2,083

citations
Automatic Differentiation in Machine Learning: a Survey
2015 Maynooth University ePrints and eTheses Archive (Maynooth University) article Computer Science Gaussian Processes and Bayesian Inference Open access

2,030

citations
Solving high-dimensional partial differential equations using deep learning
2018 Proceedings of the National Academy of Sciences article Physics and Astronomy Model Reduction and Neural Networks Open access

1,787

citations
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
2017 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access

965

citations
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
2021 Science Advances article Physics and Astronomy Model Reduction and Neural Networks Open access

834

citations
Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems
2021 IEEE Transactions on Knowledge and Data Engineering article Computer Science Machine Learning and Algorithms Open access

814

citations
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook
2020 Journal of Manufacturing Science and Engineering article Decision Sciences Impact of AI and Big Data on Business and Society Open access

553

citations
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
2018 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access

431

citations
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
2017 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access

319

citations
Can physics-informed neural networks beat the finite element method?
2024 IMA Journal of Applied Mathematics article Physics and Astronomy Model Reduction and Neural Networks Open access

219

citations
Flow Analysis of Injection Molds
2013 other Engineering Injection Molding Process and Properties

207

citations
Reinforcement Learning: State-of-the-Art
2012 article Computer Science Reinforcement Learning in Robotics

189

citations
Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks
2021 The International Journal of Advanced Manufacturing Technology article Engineering Injection Molding Process and Properties Open access cited by 1 patent

97

citations
Transfer learning with artificial neural networks between injection molding processes and different polymer materials
2021 Journal of Manufacturing Processes article Engineering Injection Molding Process and Properties cited by 1 patent

93

citations
A Review on Machine Learning Models in Injection Molding Machines
2022 Advances in Materials Science and Engineering article Engineering Injection Molding Process and Properties Open access

89

citations
Control of properties in injection molding by neural networks
2001 Engineering Applications of Artificial Intelligence article Engineering Injection Molding Process and Properties

87

citations
Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems
2022 IEEE Transactions on Energy Conversion article Physics and Astronomy Model Reduction and Neural Networks Open access

74

citations