Sparse defects detection and 3D imaging base on electromagnetic tomography and total variation algorithm.

Area of Science:

• Materials Science
• Non-destructive Testing
• Electromagnetic Methods

Background:

• Metal products frequently develop internal defects like holes, dents, and scratches.
• Detecting these defects is crucial for industrial applications.
• Electromagnetic tomography (EMT) shows promise for defect imaging but faces challenges with small, sparse defects.

Purpose of the Study:

• To enhance image quality for metal defect reconstruction using EMT.
• To address the challenges of imaging small and sparsely distributed metal defects.
• To develop a more effective mathematical model for EMT reconstruction.

Main Methods:

• Utilized a sparse regularization method for the EMT mathematical model.
• Employed three-dimensional reconstructed images based on detection depth and excitation frequency.
• Conducted both simulations and experimental validations.

Main Results:

• The sparse regularization method significantly improved image reconstruction quality.
• Successfully visualized surface and internal defects in metal parts.
• Demonstrated the effectiveness of the proposed method through simulations and experiments.

Conclusions:

• Sparse regularization is an effective approach for improving EMT-based metal defect imaging.
• The developed method provides a viable solution for detecting challenging small and sparse defects.
• This technique holds potential for quality control in metal product manufacturing.