| --- |
| 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 |
