Tool wear prediction based on hybrid feature selection

Wanzhen Wang; Sze Song Ngu; Miaomiao Xin; Xiaomei Ni; Qian Hu; Ting Wang

doi:10.1117/12.3032833

5 July 2024 Tool wear prediction based on hybrid feature selection

Wanzhen Wang, Sze Song Ngu, Miaomiao Xin, Xiaomei Ni, Qian Hu, Ting Wang

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131846Q (2024) https://doi.org/10.1117/12.3032833
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Tool wear monitoring can provide information and improve productivity for tool change decisions in Computer Numerical Control (CNC) machine manufacturing. The processing of indirect signals and extracting sensitive features related to tool wear are critical. In this paper, a hybrid feature selection method is proposed and verified by improved Long Short-Term Memory (LSTM) models. Firstly, data preprocessing is performed on the acquired vibration, force, and acoustic emission (AE) signals and relevant features are extracted from multi-domains and preliminary filtered using a mixture of the SRCC and MI methods. Then, feature dimensionality is further reduced by combining dimensionality reduction methods. After the sensitive features are enhanced by the self-attention mechanism, temporal dependencies are extracted by the LSTM layer. Finally, tool wear is predicted using the fully connected layers. The experiment shows that the combined feature extraction strategy can select sensitive features more effectively and improve the prediction performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wanzhen Wang, Sze Song Ngu, Miaomiao Xin, Xiaomei Ni, Qian Hu, and Ting Wang "Tool wear prediction based on hybrid feature selection", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131846Q (5 July 2024); https://doi.org/10.1117/12.3032833

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Feature selection

Feature extraction

Signal processing

Deep learning

Principal component analysis

Image processing

Machine learning

Show All Keywords

Keywords/Phrases

Search In:

Publication Years