paper-with-me

홈 › Papers

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

2024-07-04 · Anushrut Jignasu, Kelly O. Marshall, Ankush Kumar Mishra, Lucas Nerone Rillo, Baskar Ganapathysubramanian, Aditya Balu, Chinmay Hegde, Adarsh Krishnamurthy

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). SLICE-100K will be the first step in developing a multimodal foundation model for digital manufacturing.

📄 PDF Abstract BibTeX arXiv:2407.04180

Code (0)

등록된 구현이 없습니다.

Tasks

Code TranslationInstance SegmentationSemantic Segmentation

Methods 이 논문이 사용한 방법론

Refunds@Expedia|||How do I get a full refund from Expedia? “How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Quick Help &…
Attention 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Weight Decay 설명 없음
Discriminative Fine-Tuning Discriminative Fine-Tuning is a fine-tuning strategy that is used for ULMFiT type models. Instead of using the same learning rate…
Multi-Head Attention 설명 없음
Residual Connection 설명 없음
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Similar Papers 제목 키워드 기반

Force Controlled Printing for Material Extrusion Additive Manufacturing

2024-03-24 · Xavier Guidetti, Nathan Mingard, Raul Cruz-Oliver, Yannick Nagel 외

In material extrusion additive manufacturing, the extrusion process is commonly controlled in a feed-forward fashion. The amount of material to be extruded at each printing location is pre-computed by a planning software…

PrintAnything: Learning an Intermediate Representation for 3D printing G-code Generation

2026-07-30 · Sangmin Hong, Daniel Sungho Jung, Heewon Kim, Kyoung Mu Lee arxiv

Point clouds are one of the most fundamental and widely used 3D representations, serving as the most basic geometric representation of 3D shapes. Nevertheless, most existing 3D printing pipelines require a watertight mes…

Code GenerationPoint Clouds

Numerical modeling of hydrogel scaffold anisotropy during extrusion-based 3D printing for tissue engineering

2023-10-04 · V. T. Mai, R. Chatelin, E. -J. Courtial, C. Boulocher 외

Extrusion-based 3D printing is a widely utilized tool in tissue engineering, offering precise 3D control of bioinks to construct organ-sized biomaterial objects with hierarchically organized cellularized scaffolds. The i…

Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion

2020-02-06 · Caelan Reed Garrett, Yijiang Huang, Tomás Lozano-Pérez, Caitlin Tobin Mueller

There is increasing demand for automated systems that can fabricate 3D structures. Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D printing due to a manipulator's flexibility …

An Efficient and Uncertainty-aware Reinforcement Learning Framework for Quality Assurance in Extrusion Additive Manufacturing

2025-03-02 · Xiaohan Li, Sebastian Pattinson

Defects in extrusion additive manufacturing remain common despite its prevalent use. While numerous AI-driven approaches have been proposed to improve quality assurance, the inherently dynamic nature of the printing proc…

Q-LearningUncertainty QuantificationZero-Shot Learning