# Industrial Application of AI-Based Assistive Magnetic Particle Inspection
## 5 Results
### Defect Length Measurement  
Length measurement accuracy is defined as MAE on four artificially created ground truth straight paths representing defects. Those paths are straight lines ranging from 20 to 60 mm drawn on stickers which are applied on the curved surface of a real customer's part, as depicted in Figure 18. The results are summarized in Table 5. The computed MAE is 0.925 mm. We can see that MAE is not proportional to defect length. Indeed, the operator draws the defect line on an unzoomable image. This leads to operator-dependant position errors.  
Figure 17: Example of detections on testset with correct detection (TP), missing detection (FN) and false detection (FP).  
Figure 18: An example part with ground truth defect length (20 to 60 mm).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ground Truth (mm)** & **Measured Value (mm)** & **Relative Error (\%)** & **Absolute Error (mm)** \\ \hline
50.0 & 51.8 & 3.6 & 1.8 \\
20.0 & 20.0 & 0.0 & 0.0 \\
60.0 & 60.1 & 0.2 & 0.1 \\
30.0 & 28.2 & 6.0 & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Defect length measurement performance.  
Table 6 summarized the PARADES machine performances compared to previous work.  
The paper [7] shows an industrial machine used to detect flaws longer than 10 mm and wider than 0.3 mm on steel wire rod and bar. The system is designed for a specific production line at Kobe Work, with a dedicated eight camera system. The work carried out in [8] shows an experimental setup to detect flaws of 15 mm length with widths of 0.2 mm, 0.4 mm, 0.6 mm and 0.8 mm. The machine is dedicated for small parts, for example a bearing of size 20 mm \(\times\) 20 mm \(\times\) 20 mm. Authors in [9] present a robotic MPI defect-detection system with external PC as computing unit. The machine is used to detect small defects on big mechanical parts (a defect represents 1/4000 image size). The paper [10] presents an automatic device for MPI defect detection dedicated to crankshaft. An experimental setup is presented with one UV light bulb and a camera, plugged into an external PC as a computing unit. In the paper [11], the authors present another way of detecting defects on crankshaft using the MPI technique. Authors in [13] present a software block to detect defects with MPI on bearing rings. The hardware side is not detailed.  
As we explained in the previous paragraph, only some of the related works are industrial-grade products [9; 11]; the others are experimental setups that cannot be used in industry. In those works, the detection performance (i.e recall) is either not given or is below our expectations. Only [9; 11; 13] perform better than our needs. In addition, some presented machines are dedicated to a chosen mechanical part; the system cannot be used for another part. Only [10; 11] can be used with several mechanical parts. None of the presented related works offer a tool to measure defect length nor allow for wide mechanical part image acquisition (600 \(\times\) 400 mm).  
Based on this comparative study, we show that none of the previous works had success building a versatile and automatic defect-detection machine in MPI which can be used in production lines. Some works present advanced mechanical construction, but specifically dedicated to a kind of mechanical part. Other works show improvement in terms of defect-detection performance, but with an experimental and non-industrial machine that cannot be used in production lines. The PARADES machine gathers the following two important features: a versatile production-ready mechanical machine and high-performance defect-detection software. In addition, the PARADES machine provides a non-existing tool to accurately measure the defect length whatever the distance between the part and the machine.