Three-Dimensional Printed Subsurface Defect Detection by Active Thermography Data-Processing Algorithm

This article evaluates an active thermography algorithm to detect subsurface defects in materials made by additive manufacturing (AM). It is based on the techniques of thermographic signal reconstruction (TSR), thermal contrast, and the physical principles of heat transfer. The subsurface defects have different infill, depth, and size. The results obtained from this algorithm are compared with state-of-the-art TSR technique and show the high performance of the proposed algorithm even for subsurface defects done by 3D AM. The resulting images are better shown using the absolute difference in the place of variance. The proposed algorithm has higher contrast, better sensitivity to the defect depths, and lower noise than the TSR. The resultant image is quite clean and gives no doubt where the subsurface defects are.

Automated summary

This article evaluates an active thermography algorithm for detecting subsurface defects in materials produced by additive manufacturing. The algorithm utilizes thermographic signal reconstruction, thermal contrast, and principles of heat transfer. The algorithm outperforms state-of-the-art TSR technique in terms of contrast, sensitivity to defect depths, and noise reduction, resulting in clearer images that accurately depict the location of subsurface defects.