非破壞性膜厚AI檢測技術

表面鍍膜技術廣泛應用於各種工業製程，以提升材料的耐磨性、耐蝕性等。然而鍍膜層的厚度對於最終產品的性能至關重要，若鍍層厚度過厚或過薄皆可能會影響工件之功能性，導致產品失效。一般常見的傳統檢測方法多透過破壞樣品後，使用目視、顯微鏡或SEM等工具進行量測，雖可提供準確的測量結果，但檢測效率低且會造成樣品破壞問題。本研究透過人工智慧(Artificial intelligence, AI)技術，結合深度學習與影像處理演算法，針對鍍膜表面的膜厚進行分析，克服傳統破壞性檢測的侷限性，並可適應不同材質與鍍層類型，減少雜訊造成的干擾，以確保產品的品質與一致性。

Surface coating technology is widely applied in various industrial processes to enhance material properties such as wear resistance and corrosion resistance. However, the quality and thickness of the coating layer are crucial to the final product's performance. If the coating thickness is too thick or too thin, it may affect the functionality of the workpiece, leading to product damage or failure. Traditional inspection methods typically involve destructive testing, such as mechanical cutting, followed by visual inspection, microscopy, or SEM (Scanning Electron Microscopy) analysis. While these methods provide accurate measurements, they suffer from low efficiency and cause damage to the sample. This study employs artificial intelligence (AI) technology, integrating deep learning and image processing algorithms to analyze coating thickness. By overcoming the limitations of traditional destructive testing, this approach can adapt to various materials and coating types while reducing interference caused by noise, ensuring product quality and consistency.