# The Prediction and Evaluation of Surface Quality during the Milling of Blade-Root Grooves Based on a Long Short-Term Memory Network and Signal Fusion

## TL;DR

This paper introduces a method to predict and evaluate the surface quality of milled blade-root grooves in turbine blades using sensor data and machine learning.

## Contribution

A novel surface texture prediction method using fused acoustic vibration signals and texture image features is proposed.

## Key findings

- A texture feature prediction model was established with 90% accuracy in evaluating surface quality.
- Signal fusion and feature extraction techniques improved the prediction of surface texture features.
- Wavelet denoising and multivariate smoothing enhanced the quality of sensor signals for analysis.

## Abstract

The surface quality of milled blade-root grooves in industrial turbine blades significantly influences their mechanical properties. The surface texture reveals the interaction between the tool and the workpiece during the machining process, which plays a key role in determining the surface quality. In addition, there is a significant correlation between acoustic vibration signals and surface texture features. However, current research on surface quality is still relatively limited, and most considers only a single signal. In this paper, 160 sets of industrial field data were collected by multiple sensors to study the surface quality of a blade-root groove. A surface texture feature prediction method based on acoustic vibration signal fusion is proposed to evaluate the surface quality. Fast Fourier transform (FFT) is used to process the signal, and the clean and smooth features are extracted by combining wavelet denoising and multivariate smoothing denoising. At the same time, based on the gray-level co-occurrence matrix, the surface texture image features of different angles of the blade-root groove are extracted to describe the texture features. The fused acoustic vibration signal features are input, and the texture features are output to establish a texture feature prediction model. After predicting the texture features, the surface quality is evaluated by setting a threshold value. The threshold is selected based on all sample data, and the final judgment accuracy is 90%.