Datasets:
File size: 2,036 Bytes
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license: cc-by-4.0
task_categories:
- visual-question-answering
language:
- en
tags:
- manufacturing
- additive-manufacturing
- directed-energy-deposition
- ded
- thermal-imaging
- vqa
pretty_name: DED Thermal Visual Question Answering (DD4)
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: train.parquet
- split: test
path: test.parquet
---
# DED-Thermal VQA Dataset
This dataset is designed for **Visual Question Answering (VQA)** in the domain of advanced manufacturing, specifically focusing on **Directed Energy Deposition (DED)** additive manufacturing processes. It pairs in-situ infra-red (IR) thermal monitoring sequences/images with structured question-answer pairs to facilitate the development of Vision-Language Models (VLMs) capable of understanding physical phenomenon and thermal history in 3D printing.
---
## Dataset Summary
During the Directed Energy Deposition (DED) process, real-time thermal behavior (e.g., melt pool dynamics, cooling rates, and thermal gradients) is critical to the structural integrity and quality of the final printed components.
dataset provides a benchmark for evaluating multimodal AI agents on their ability to interpret, reason, and answer questions about physical manufacturing dynamics based on thermal visualization data.
* **Task**: Visual Question Answering (VQA)
* **Domain**: Additive Manufacturing / Directed Energy Deposition (DED)
* **Modality**: Multi-modal (Thermal Imaging + Natural Language)
* **Format**: Parquet (compatible with Hugging Face `datasets` library)
---
## Dataset Structure
The directory structure of this dataset is as follows:
```text
vqa_ded_thermal/
├── train.parquet # Training split containing VQA pairs and image features/references
├── test.parquet # Testing split for model evaluation
├── dataset_report.json # Statistical overview and structural metadata of the dataset
└── README.md # Dataset card and documentation |