---
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