A robust framework for FFF process planning

Paito houses all of my FFF algorithms and is used to power other projects by providing a stable well tested framework on which to build new and more interesting algorithms. It provides the necessary basics to parse meshes, modify gcode, and apply optimization algorithms through a series of well defined, iterator based interfaces.

By using best practices for software engineering I have learned from my work as a software engineer, I have created a well tested and robust system that serves as a solid base for producing FFF algorithm publications.

Designing software for research applications rather than production applications provides an interesting challenge. In production code, you can often depend on a well defined set of requirements that stabilize as the software matures. In research code, there is no maturation, nor a stable set of requirements. Building around this principle means leaning as much as possible into a generic yet very simple set of well defined interfaces between different types of algorithms that depend on a set of very well tested geometric and mathematical primitives.

Visualization of a toolpath generated for a model. The colors signify the feedrate used during the printing of each motion segment.
Geometry/time optimized print using paito 3D-aware printing. 20% faster than layer-by-layer print of the same model.