| --- |
| license: cc-by-4.0 |
| task_categories: |
| - visual-question-answering |
| - image-classification |
| language: |
| - en |
| tags: |
| - smart-manufacturing |
| - additive-manufacturing |
| - lpbf |
| - anomaly-detection |
| - vlm-sft |
| - powder-bed-fusion |
| - optical-tomography |
| size_categories: |
| - 1K-10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "default/train-*" |
| - config_name: ot |
| data_files: |
| - split: train |
| path: "ot/train-*" |
| - config_name: pb |
| data_files: |
| - split: train |
| path: "pb/train-*" |
| --- |
| |
| # Multi-Modal LPBF Anomaly Detection Dataset (PB & OT) |
|
|
| ## 1. Overview |
| This dataset is a highly structured, industry-aligned multi-modal VQA dataset designed for fine-tuning and benchmarking **Vision-Language Models (VLMs)** (such as Qwen2-VL, LLaVA, and CogVLM) on **Laser Powder Bed Fusion (PBF-LB / LPBF)** manufacturing anomaly diagnostics. |
|
|
| The dataset integrates two complementary physical monitoring modalities from the LPBF printing process: |
| - **Powder Bed (PB) Images:** Captured before laser melting. Excellent for detecting mechanical and geometric anomalies (e.g., blade recoating streaks, part warping, incomplete powder spreading). |
| - **Optical Tomography (OT) Images:** Captured during the laser melting phase. Essential for monitoring localized thermal accumulation, melt-pool instabilities, and keyhole defects. |
|
|
| By hosting both modalities in a single, unified repository with split-loading capabilities, this dataset enables researchers to train all-encompassing diagnostics models as well as perform robust ablation studies on sensor modalities. |
|
|
| --- |
|
|
| ## 2. Dataset Architecture & Schema |
|
|
| Every sample in this dataset is formatted using a highly standardized, enterprise-grade schema optimized for high-throughput SFT (Supervised Fine-Tuning) and secure data deduplication. |
|
|
| ### Schema Fields |
| | Field Name | Type | Description | |
| | :--- | :--- | :--- | |
| | `query` | `String` | The system prompt/instruction directing the VLM to evaluate the printing layer. | |
| | `image` | `Image` | The serialized input image (PB or OT modality) wrapped in Apache Arrow. | |
| | `annot` | `String` | Ground-truth categorical classification label. Strictly mapped to `good` or `anomalous`. | |
| | `reasoning` | `String` | Reserved for Chain-of-Thought (CoT) explanations (currently `null` for base SFT). | |
| | `cate` | `String` | Category identifier. Default is `"B"` (indicating standard industrial SFT). | |
| | `task` | `String` | Global unique task ID. Mapped as `"T-defect"`. | |
| | `metadata` | `String (JSON)` | Serialized metadata including modality info, original paths, and image SHA256 hashes for deduplication. | |
| | `mask` | `Image` | Reserved field for binary pixel-level defect segmentation map (currently `null`). | |
| | `masks` | `List[Image]` | Reserved field for multi-instance segmentation masks (currently `null`). | |
|
|
| --- |
|
|
| ## 3. Dataset Subsets (Configs) |
|
|
| To support diverse experimental setups, we utilize Hugging Face's multi-configuration manager (`configs`) to split the dataset into three logical streams under a single repository: |
|
|
| ### Subset Breakdown |
| - **`default`**: A balanced, shuffled mixture of both PB and OT samples. Recommended for training unified, general-purpose LPBF QA agents. |
| - **`ot`**: Contains purely Optical Tomography images and corresponding anomaly annotations. |
| - **`pb`**: Contains purely Powder Bed visible light images and corresponding surface defect annotations. |
|
|
| ### Quick Start (How to Load) |
|
|
| You can flexibly stream or download any of the configurations using the Hugging Face `datasets` library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # 1. Load the merged multi-modal dataset (Default) |
| dataset_full = load_dataset("AI4Manufacturing/lpbf-structured-dataset") |
| |
| # 2. Load the OT thermal emission subset only |
| dataset_ot = load_dataset("AI4Manufacturing/lpbf-structured-dataset", "ot") |
| |
| # 3. Load the PB surface morphology subset only |
| dataset_pb = load_dataset("AI4Manufacturing/lpbf-structured-dataset", "pb") |
| |
| # Example: Print a sample from the loaded config |
| print(dataset_ot["train"][0]) |
