Phase field modeling, calibration and experimental design for Stereolithography in 3D printing

Industry 4.0 is a concept that attempts to revolutionize the way we manufacture products in factories. Through recent progresses in Internet-of-Things, cloud computing and artificial intelligence, companies can now capitalize on real-time visibility of the status and progress occurring in production, and have begun gradually transitioning towards a new manufacturing format with minimal human input and increased automation. At the centre of this paradigm shift, 3D printing technologies are expected to play a significant role.

Stereolithography, as one of the earliest 3D printing technologies, builds objects in a layer-by-layer fashion by using an ultraviolet laser to solidify liquid polymer resin. Its main application lies in crafting high resolution 3D models of anatomical regions for medical training and surgery planning. However, objects created from the same design can exhibit undesirable variations in mechanical properties due to small differences in the printing environment. These variations can be minimized by combining data collection with well-placed sensors and numerical prediction of mechanical properties from calibrated mathematical models, which then guide the appropriate adjustment of the construction apparatus.

We plan to provide a mathematical framework of the above strategy for stereolithography. The main goals of this proposal are to develop and calibrate some novel mathematical models, and explore sensor configurations minimizing uncertainty in the collected data to facilitate model calibration. The key ingredient for the modeling is the phase field approach that produces mathematical descriptions amenable to further theoretical and numerical analysis. Then, we tackle the problems of calibration and sensor placement with the framework of optimal experimental design. The proposed modeling and accompanying investigations can also be applied to other 3D printing technologies, and thus we envision the ideas developed here can contribute towards resolving other challenges encountered in the advent of Industry 4.0.