Evaluating the printability of stl files with ML
This addresses a practical problem for novice 3D printing users, though it appears incremental as it builds on existing slicing software checks.
The paper tackles the problem of 3D printing failures by training an AI model to detect issues in STL files, aiming to assist less experienced users before printing begins.
3D printing has long been a technology for industry professionals and enthusiasts willing to tinker or even build their own machines. This stands in stark contrast to today's market, where recent developments have prioritized ease of use to attract a broader audience. Slicing software nowadays has a few ways to sanity check the input file as well as the output gcode. Our approach introduces a novel layer of support by training an AI model to detect common issues in 3D models. The goal is to assist less experienced users by identifying features that are likely to cause print failures due to difficult to print geometries before printing even begins.