Slice-100K: A Multimodal Dataset for Extrusion-based 3D Printing

TL;DR

Slice-100K is a comprehensive dataset of over 100,000 G-code files with associated CAD models, enabling advancements in AI-driven additive manufacturing and G-code translation.

Contribution

The paper introduces Slice-100K, the first large-scale curated dataset linking G-code with CAD models, geometric data, and renderings for digital manufacturing research.

Findings

Successfully fine-tuned GPT-2 for G-code translation tasks.

Demonstrated the dataset's utility in developing multimodal foundation models.

Provided a publicly accessible resource for the manufacturing AI community.

Abstract

G-code (Geometric code) or RS-274 is the most widely used computer numerical control (CNC) and 3D printing programming language. G-code provides machine instructions for the movement of the 3D printer, especially for the nozzle, stage, and extrusion of material for extrusion-based additive manufacturing. Currently, there does not exist a large repository of curated CAD models along with their corresponding G-code files for additive manufacturing. To address this issue, we present Slice-100K, a first-of-its-kind dataset of over 100,000 G-code files, along with their tessellated CAD model, LVIS (Large Vocabulary Instance Segmentation) categories, geometric properties, and renderings. We build our dataset from triangulated meshes derived from Objaverse-XL and Thingi10K datasets. We demonstrate the utility of this dataset by finetuning GPT-2 on a subset of the dataset for G-code translation from a legacy G-code format (Sailfish) to a more modern, widely used format (Marlin). Our dataset can be found at https://github.com/idealab-isu/Slice-100K. Slice-100K will be the first step in developing a multimodal foundation model for digital manufacturing.