DYNA JOURNAL ENGINEERING

Key words:

Computational Spectral Imaging, Non-Destructive Testing, Industry 4.0, Smart Manufacturing, Hyperspectral Imaging, Defect Detection, Compressed Sensing Reconstruction, Deep Learning Vision Transformer, Real-time Quality Control Automation, Spectral Attention Mechanism, Material Defect Classification, Production Line Inspection System, End-to-End Feature Extraction, Multi-modal Sensor Fusion, Automotive Manufacturing Validation, Industrial Image Segmentation

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

In the framework of Industry 4.0, the requirement for high accuracy and efficiency in non-destructive testing technology continues to rise. Traditional methods can be inaccurate and slow in complex situations. This paper introduces a new inspection solution based on computational spectral imaging. This method builds develops a multispectral imaging system to collect high-dimensional data with defect detection and imaging quality improved by feature extraction and spectral merging. The experiments demonstrate noticeable performance of the proposed method for typical industrial material inspections with root mean square error of 2.16×10?², peak signal-to-noise ratio of 35.42 dB, and structural similarity of 0.987. The single-sample inspection time is reduced to approximately 62% of the original based on traditional three-dimensional convolutional neural networks, with an accuracy above 95% at a noise s of 0.065. The Kappa consistency coefficient for production line verification is also 0.924, with research showing that this system provides significant advantages for improved detection accuracy and level of automation - offering a practical solution for high-quality, low-cost intelligent detection in some manner or another in the Industry 4.0 environment.
Keywords: Computational Spectral Imaging (CSI), Non-Destructive Testing (NDT), Industry 4.0, Smart Manufacturing, Hyperspectral Imaging, Defect Detection