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<p>The integration of artificial intelligence in industry is crucial for realizing Industry 4.0; however, the lack of industrial datasets remains a significant challenge. While several generative AI methods have been proposed to create synthetic data, these approaches are often inefficient and require a large volume of training data to function effectively. In this study, we utilize a physics-based rendering procedure to generate a synthetic dataset of aeroengine blades. This dataset is then used to train a defect inspection model, thereby addressing data scarcity and enhancing defect detection accuracy in industrial applications. The dataset generation process begins with preparing Computer-Aided Design (CAD) models and material textures, then constructing a realistic inspection scene incorporating domain-randomized camera settings, lighting, and background elements. The generated data is assessed for effectiveness in both supervised and unsupervised defect detection tasks. Additionally, sim-to-real transferability is examined, demonstrating that models trained on the generated synthetic data can effectively detect and classify defects in real blade images.</p>

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BladeSynth: A High-Quality Rendering-Based Synthetic Dataset for Aero Engine Blade Defect Inspection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月19日

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  • M. A. Mohammed Eltoum,
  • Ehtesham Iqbal,
  • Yahya Zweiri,
  • Brain Moyo,
  • Yusra Abdulrahman

摘要

The integration of artificial intelligence in industry is crucial for realizing Industry 4.0; however, the lack of industrial datasets remains a significant challenge. While several generative AI methods have been proposed to create synthetic data, these approaches are often inefficient and require a large volume of training data to function effectively. In this study, we utilize a physics-based rendering procedure to generate a synthetic dataset of aeroengine blades. This dataset is then used to train a defect inspection model, thereby addressing data scarcity and enhancing defect detection accuracy in industrial applications. The dataset generation process begins with preparing Computer-Aided Design (CAD) models and material textures, then constructing a realistic inspection scene incorporating domain-randomized camera settings, lighting, and background elements. The generated data is assessed for effectiveness in both supervised and unsupervised defect detection tasks. Additionally, sim-to-real transferability is examined, demonstrating that models trained on the generated synthetic data can effectively detect and classify defects in real blade images.