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Related Experiment Video

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Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

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Machine Learning and 3D Reconstruction of Materials Surface for Nondestructive Inspection.

Oleg O Kartashov¹, Andrey V Chernov¹, Alexander A Alexandrov¹

¹The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

This study introduces a cost-effective visual testing method for steel pipeline welds using Raspberry Pi and AI. The system achieves high accuracy in detecting weld defects, offering an alternative to expensive traditional methods.

Keywords:

computer vision machine learning material surface reconstruction nondestructive testing technologies and systems

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Area of Science:

Materials Science
Computer Science
Mechanical Engineering

Background:

Steel pipeline installation relies on robust butt weld integrity.
Current automated X-ray and ultrasonic testing methods are resource-intensive and costly.
There is a need for economical preliminary quality control solutions.

Purpose of the Study:

To develop an affordable visual testing system for preliminary steel pipeline weld quality control.
To leverage computer vision and machine learning for automated defect detection.
To create a hardware platform using Raspberry Pi for this purpose.

Main Methods:

Development of a hardware platform using Raspberry Pi 4.
Implementation of computer vision algorithms, specifically YOLOv5 for object detection.
Application of machine learning models, including random forest, for defect classification.
Utilizing weld contours and point clouds of weld surfaces for analysis.

Main Results:

The YOLOv5 algorithm achieved a mean average precision (mAP) of 86.9% for weld defect detection based on contours.
A YOLOv5 model trained on control objects reached a mAP of 96.8%.
The random forest model achieved an 87.5% mAP for identifying defect precursors from surface point clouds.

Conclusions:

The proposed system offers a viable, cost-effective alternative for preliminary weld quality assessment.
Computer vision and machine learning integration provides efficient defect detection capabilities.
This approach can significantly reduce costs associated with traditional pipeline weld inspection.