# Evaluating the printability of stl files with ML

arXiv: https://arxiv.org/abs/2509.12392 · HTML: https://mlsanity.com/paper/2509.12392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning](https://mlsanity.com/paper/2410.08114/) (2024)
- [Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook](https://mlsanity.com/paper/2311.02608/) (2023)
- [A Characterization of 3D Printability](https://mlsanity.com/paper/2010.12930/) (2020)

## Built on by (most cited first, 1 of 1)

- [Task-Driven 3D Printability Assistance via Geometry- and Knowledge-Grounded LLM Reasoning](https://mlsanity.com/paper/2608.22128/) (2026)

## Abstract (arXiv)

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.