The MatlabVAM project is a collection of Matlab scripts and functions for performing iterative tomographic optimization for Volumetric Additive Manufacturing [1]. This code is intended to be used as a reference for those looking to implement similar algorithms in other software packages, or to aid in the development of new algorithms for tomographic optimization.
Notably, this project is not a stand alone software like MatlabCAL or VAMToolBox. It is intended to complement academic understanding and research toward material-aware modelling within the tomographic optimization loop.
- Matlab R2025b or later
- Image Processing Toolbox
Tomographic Optimization Naive Implementation of 2D tomographic optimization algorithms, including:
- Computed Axial Lithography (CAL) [2]
- Object Space Model Optimization (OSMO) [3]
Naive implementations do not consider a material response function (i.e. curing response) beyond a simple linear threshold or any optical effects like absorbance or scattering.
Intensity Dependent Kinetics Intesity dependent curing based on the work of Tu et. al for acrylate photo-resins [4]. Example implementations to model Jacobs working curve.
Material-Aware Tomographic Optimization Application of optical attenuation, kinetic response, and tomographic optimization to generate spatiotemporal cure response based on:
- Estimated attenuated photodose
- Estimated fractional conversion
- Estimated curing height
[1] Kelly, B. E., et al. Volumetric additive manufacturing via tomographic reconstruction. Science 363.6431 (2019): 1075–1079. https://doi.org/10.1126/science.aau7114 [2] Kelly, B., Bhattacharya, I., Shusteff, M., Panas, R. M., Taylor, H. K., & Spadaccini, C. M. (2017). Computed axial lithography (CAL): toward single step 3D printing of arbitrary geometries. arXiv preprint arXiv:1705.05893. [3] Rackson, C. M., Champley, K. M., Toombs, J. T., Fong, E. J., Bansal, V., Taylor, H. K., ... & McLeod, R. R. (2021). Object-space optimization of tomographic reconstructions for additive manufacturing. Additive manufacturing, 48, 102367. [4] Tu, Jianwei, et al. "A practical framework for predicting conversion profiles in vat photopolymerizations." Additive Manufacturing 59 (2022): 103102.