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In this work, we study a non-destructive evaluation for metal parts in the automotive industry to detect and characterize anomalies such as thinning or lack of material. We simulate data from an infrared camera by solving the heat equation using the finite element method, with these data, we determine the location and type of imperfection. The inverse problem of this non-destructive testing is carried out by a Bayesian approach, considering 70, 80, and 90 percent of percent weight loss and lack of material, detecting all these types of imperfections. With this scheme, the minimum size of imperfections detected was 1cm per side.

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Bayesian Approach to Characterize Imperfections in Metal Parts Using Infrared Detection

  • 会议论文

  • 发表日期：

    2026年

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  • Lili Guadarrama Bustos,
  • Brenda Patricia Prieto Garcia,
  • Luz Judith Rodríguez Esparza

摘要

In this work, we study a non-destructive evaluation for metal parts in the automotive industry to detect and characterize anomalies such as thinning or lack of material. We simulate data from an infrared camera by solving the heat equation using the finite element method, with these data, we determine the location and type of imperfection. The inverse problem of this non-destructive testing is carried out by a Bayesian approach, considering 70, 80, and 90 percent of percent weight loss and lack of material, detecting all these types of imperfections. With this scheme, the minimum size of imperfections detected was 1cm per side.