Evaluating the printability of stl files with ML
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914039073865728 |
|---|---|
| author | Henn, Janik Hauptmannl, Adrian Gardi, Hamza A. A. |
| author_facet | Henn, Janik Hauptmannl, Adrian Gardi, Hamza A. A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12392 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating the printability of stl files with ML Henn, Janik Hauptmannl, Adrian Gardi, Hamza A. A. Machine Learning Artificial Intelligence 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. |
| title | Evaluating the printability of stl files with ML |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.12392 |