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<p>Surface quality control plays a&#xa0;crucial role in ensuring product quality and performance. However, traditional offline inspection methods, which only examine products after completion, often result in late defective detection and resource waste. To address this limitation, online surface roughness measurement using non-contact methods has emerged as a&#xa0;promising solution. This method enables continuous monitoring of surface quality during the manufacturing process, facilitating early detection of issues and timely adjustments to process parameters. Particularly in grinding processes, the application of this technique yields significant benefits. By combining 2D surface imaging with a&#xa0;wavelet approach, detailed information about the surface texture, including parameters such as time delay, embedding dimension, and nearest neighbors, can be extracted. With the ability to accurately predict future surface roughness, the manufacturing process becomes more proactive. Predicting surface quality helps optimize cycle time, ensure product consistency, and minimize defects. Consequently, production efficiency is significantly improved.</p>

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Surface roughness prediction in grinding processes using time-series image analysis and wavelet embedding

  • 期刊论文

  • 发表日期：

    2025年8月12日

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  • Tran Thi Hien,
  • Nguyen Thi Hien

摘要

Surface quality control plays a crucial role in ensuring product quality and performance. However, traditional offline inspection methods, which only examine products after completion, often result in late defective detection and resource waste. To address this limitation, online surface roughness measurement using non-contact methods has emerged as a promising solution. This method enables continuous monitoring of surface quality during the manufacturing process, facilitating early detection of issues and timely adjustments to process parameters. Particularly in grinding processes, the application of this technique yields significant benefits. By combining 2D surface imaging with a wavelet approach, detailed information about the surface texture, including parameters such as time delay, embedding dimension, and nearest neighbors, can be extracted. With the ability to accurately predict future surface roughness, the manufacturing process becomes more proactive. Predicting surface quality helps optimize cycle time, ensure product consistency, and minimize defects. Consequently, production efficiency is significantly improved.