# Denoising and Simplification of 3D Scan Data of Damaged Aero-Engine Blades for Accurate and Efficient Rigid and Non-Rigid Registration

## TL;DR

This paper introduces a method to clean and simplify 3D scan data of damaged aero-engine blades to improve inspection and remanufacturing accuracy and efficiency.

## Contribution

A new methodology for denoising and simplifying 3D scan data while preserving critical geometry in damaged regions.

## Key findings

- The method effectively removes outliers and measurement noise from scan data.
- Down-sampled datasets significantly reduce computational time with minimal accuracy loss.
- Experimental results validate improved efficiency in rigid and non-rigid registration processes.

## Abstract

Point cloud processing of raw scan data is a critical step to enhance the accuracy and efficiency in computer-aided inspection and remanufacturing of damaged aero-engine blades. This paper presents a new methodology to obtain a noise-reduced and simplified dataset from the raw scan data while preserving the underlying geometry of the damaged blade in high-curvature and damaged regions. At first, outliers are removed from the scan data, and measurement noise is reduced through local least-squares quadric surface/plane fitting on the adaptive support domain of measured points under the measurement uncertainty constraint of inspection data. Then, a directed Hausdorff distance-based region growing scheme is developed to progressively search within the support domain of denoised data points to obtain a down-sampled dataset while preserving the local geometric shape of the surface. Numerical and experimental case studies have been conducted to evaluate the accuracy and computation time of scan-to-CAD rigid registration and CAD-to-scan non-rigid registration processes using the down-sampled dataset of damaged blades. The results have demonstrated that the proposed methodology effectively removes the measurement noise and outliers and provides a down-sampled dataset from the scan data that can significantly reduce the time complexity of the computer-aided inspection and remanufacturing process of the point cloud of damaged blades with a negligible loss of accuracy.