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Adaptive radius filtering and PointCleanNet-based denoising method for repairing TBM milling cutter rings.

Huadong Zheng, Yunlong Zhang, Linxiao Liu

|June 10, 2026

Summary

This study introduces a hybrid method for denoising 3D scan data, improving cutting tool restoration accuracy. The approach effectively removes noise while preserving critical tool details for repair.

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Area of Science:

Engineering
Computer Science

Background:

Three-dimensional (3D) scanning is vital for precise point cloud data acquisition in cutting tool restoration.
Environmental factors and equipment errors introduce noise, compromising restoration accuracy.
Traditional noise reduction methods struggle with manual parameter adjustment and effectively removing outlier and mixed noise.

Purpose of the Study:

To develop a robust and automated noise reduction technique for 3D scanned cutting tool data.
To enhance the accuracy and efficiency of cutting tool restoration processes.
To overcome limitations of traditional noise reduction methods in handling complex noise types.

Main Methods:

A hybrid approach combining adaptive radius filtering and a deep learning model (PointCleanNet).

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Incorporation of improved normal estimation for enhanced feature extraction from point cloud data.

Integration of traditional signal processing with advanced deep learning for noise removal.

Main Results:

The proposed hybrid method effectively denoises point cloud data from 3D scanning.
The technique successfully preserves intricate details of the cutting tools during the denoising process.
Experimental results demonstrate superior performance compared to traditional noise reduction methods.

Conclusions:

The hybrid adaptive radius filtering and PointCleanNet approach offers an effective solution for denoising 3D scan data.
This method provides a high-quality data model essential for accurate cutting tool repair.
The integration of traditional and deep learning techniques advances automated tool restoration.