dr.ir. B. Nijhuis (Björn)

Engineering & Materials Science

# 3d Printers
# Hot Temperature

Chemistry

# Additive
# Heat
# Simulation

Mathematics

# Heat
# Manufacturing
# Metals

Faculty of Engineering Technology (ET), Nonlinear Solid Mechanics (NSM)

Additive manufacturing (AM) offers unprecedented design freedom. However, for production of large metal parts, undesired residual stresses and distortions limit the implementation of this technique. The localised heating of the part by the moving heat source and resulting steep thermal gradients are the cause of these drawbacks. In order to predict and circumvent unwanted effects, a fast yet accurate numerical model for part scale simulations is required. Current progress in this field has not matured to such an extent that a simulation model with tractable computational times is available. The aim of this project is to develop an efficient simulation framework for the large-scale metal AM process. To this end, alternatives to conventional modelling approaches are investigated. Where possible, the complexity of the models is reduced by using well-motivated simplifications to the physical model and model order reduction techniques. This research is conducted within the NWO Perspectief programme AiM2XL.

Nijhuis, B. , Havinga, J. , Geijselaers, B. , & van den Boogaard, T. (2023). Local model order reduction to accelerate additive manufacturing simulations. Abstract from 6th International Workshop on Model Reduction Techniques, MORTech 2023, Gif-sur-Yvette, France.

Chen, J., Xu, W., Baldwin, M. , Nijhuis, B. , van den Boogaard, T., Gutiérrez, N. G., Narra, S. P., & McComb, C. (2023). Capturing Local Temperature Evolution During Additive Manufacturing Through Fourier Neural Operator. In 43rd Computers and Information in Engineering Conference (CIE) (Vol. 2). Article DETC2023-117055 (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2). American Society of Mechanical Engineers. https://doi.org/10.1115/DETC2023-117055

Nijhuis, B. (2023). Efficient Thermomechanical Modelling of Large-Scale Metal Additive Manufacturing. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente. https://doi.org/10.3990/1.9789036556477

Nijhuis, B. , Havinga, J. , Geijselaers, B. , & van den Boogaard, T. (2022). Locally-reduced FETI for the Efficient Solution of Transient Problems with Local Nonlinearities.