Application of Machine Learning Algorithms for Tool Condition Monitoring in Milling Chipboard Process

In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in real time, which can significantly improve the effici...

Full description

Saved in:
Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 23; no. 13; p. 5850
Main Authors Przybyś-Małaczek, Agata, Antoniuk, Izabella, Szymanowski, Karol, Kruk, Michał, Kurek, Jarosław
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 23.06.2023
MDPI
Subjects
Accuracy
Acoustics
Algorithms
Artificial Intelligence
Automation
Commerce
Computer software industry
Data mining
Fourier transforms
Innovations
Machine Learning
Manufacturing
milling chipboard
Paperboard
Product quality
Production processes
Sensors
tool state monitoring
Wavelet transforms
United States
Germany
tool state monitoring
machine learning
milling chipboard
Online AccessGet full text
ISSN1424-8220
1424-8220
DOI10.3390/s23135850

Cover

More Information
Summary:In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in real time, which can significantly improve the efficiency and productivity of the manufacturing process. A combination of feature engineering and machine learning techniques was applied in order to analyze 11 signals generated during the milling process. The presented approach achieved high accuracy in detecting tool wear and predicting tool failure, outperforming traditional methods. The final findings demonstrate the potential of machine learning algorithms in improving tool condition monitoring in the manufacturing industry. This study contributes to the growing body of research on the application of artificial intelligence in industrial processes. In conclusion, the presented research highlights the importance of adopting innovative approaches to address the challenges of tool condition monitoring in the manufacturing industry. The final results provide valuable insights for practitioners and researchers in the field of industrial automation and machine learning.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s23135850