From 4D Printing to Advanced LPBF Modeling

Webinar 11

SPEAKER I

Amir A. Zadpoor (Delft University of Technology, Netherlands)

4D Printing of Medical Devices and Meta-Biomaterials

 

Abstract: Recent advances in additive manufacturing have enabled the development of 4D-printed medical devices and meta-biomaterials with programmable, stimuli-responsive functionalities. This webinar will provide an overview of the principles that promote 4D printing for biomedical applications, with a focus on multimaterial strategies, defect-driven morphing, and shape-memory-polymer-based (SMP) activation. We will discuss how spatially varying printing parameters, intentional incorporation of micro-defects, and architected material distributions can collectively generate complex shape transformations, tunable curvatures, and novel functional responses relevant to minimally invasive devices, deployable structures, and smart meta-implants. We will discuss the coupling between micro-architecture, material behavior, and programmable morphing pathways, highlighting current opportunities for designing next-generation biomedical devices with enhanced adaptability and performance.

 

Related publications:

  • Yarali, E., Mirzaali, M. J., Ghalayaniesfahani, A., Accardo, A., Diaz‐Payno, P. J., & Zadpoor, A. A. (2024). 4D printing for biomedical applications. Advanced Materials, 36(31), 2402301.
  • Moosabeiki, V., Yarali, E., Ghalayaniesfahani, A., Callens, S. J., van Manen, T., Accardo, A., Ghodrat, S., Bico, J., Habibi, m., Mirzaali, M. J., & Zadpoor, A. A. (2024). Curvature tuning through defect-based 4D printing. Communications Materials, 5(1), 10.

SPEAKER II

Mamzi Afrasiabi (ETH Zurich, Switzerland)

Revealing Lessons from LPBF Modeling

 

Abstract: Laser powder bed fusion (LPBF) is often presented as a pathway to “smart” materials and structures, yet some of the most revealing lessons emerge from modeling systems that are anything but smart. In the first part of this talk, I share insights from our high-fidelity CFD–DEM simulations of LPBF of ceramics—a material that appears simple and “unsmart,” yet exhibits peculiar melt-pool behavior, unfavorable thermophysical properties (computationally speaking!), and demanding experimental constraints that pushed our models and HPC workflows to their limits. The second part highlights multi-material LPBF, where joining steel and copper exposes fundamental interface instabilities that remain largely unaddressed in AM research. Here, I introduce our MULTI-3 framework, a fast meshfree simulation tool developed to capture multi-track, multi-layer, multi-material interactions and the mechanisms governing interface quality. My closing message is simple: how deep computational modeling of “unsmart” materials can guide truly “smart” AM?

 

Related publications:

  • Muther, A., Makowska, M.G., Zhang, Z.L., Verga, F., Marone, F., Garrivier, N., Cretton, A., Van Petegem, S., Bambach, M. and Afrasiabi, M., 2025. Identifying melt pool behavior in ceramics PBF-LB via operando synchrotron tomographic microscopy and high-fidelity process modeling.
  • Additive Manufacturing, 103, p.104756.
    Lüthi, C., Bambach, M. and Afrasiabi, M., 2025. MULTI-3: A GPU-enhanced meshfree simulation framework for multi-track, multi-layer, and multi-material laser powder bed fusion processes. Journal of Manufacturing Processes, 147, pp.29-48.