---
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])