A Novel Method for the Complex Tube System Reconstruction and Measurement

Area of Science:

• Engineering
• Computer Science
• Industrial Inspection

Background:

• Pipe structures are fundamental to many industries, transmitting heat and vibration.
• Maintaining minimum pipe distances is crucial for safety.
• Assembly errors and pipeline deformation pose significant challenges in multi-pipe systems.

Purpose of the Study:

• To develop a novel method for inspecting and reconstructing multi-pipe structures.
• To address the technical difficulties in reconstructing complex tube systems.
• To ensure the accurate 3D parameter extraction of tubes in industrial settings.

Main Methods:

• Acquiring images of the tube system from multiple positions.
• Utilizing photogrammetry technology for position calculation and structural coordination.
• Employing a convolution neural network (CNN) for edge detection of tube features.

Main Results:

• A new algorithm for tube identification and reconstruction was developed.
• The algorithm successfully extracts tube features and reconstructs 3D parameters.
• Simulation experiments and real-world aircraft engine measurements verified the algorithm's accuracy.

Conclusions:

• The proposed method offers an accurate and reliable solution for multi-pipe structure inspection.
• This technique can significantly improve the safety and integrity of industrial pipe systems.
• The validated algorithm provides a critical advancement in complex tube system reconstruction.