Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland.
Department of Mechanical Processing of Wood, Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences, 02-776 Warsaw, Poland.
Sensors (Basel). 2023 Jun 23;23(13):5850. doi: 10.3390/s23135850.
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.
在本文中，我们提出了一种使用机器学习算法监测刨花板铣削过程中刀具状态的新方法。本研究旨在解决实时检测刀具磨损和预测刀具故障的挑战，这可以显著提高制造过程的效率和生产力。我们应用了特征工程和机器学习技术的组合，以分析铣削过程中产生的 11 个信号。所提出的方法在检测刀具磨损和预测刀具故障方面达到了很高的精度，优于传统方法。最终的研究结果证明了机器学习算法在提高制造业中刀具状态监测方面的潜力。本研究为人工智能在工业过程中的应用研究领域做出了贡献。总之，本研究强调了采用创新方法来应对制造业中刀具状态监测挑战的重要性。最终的结果为工业自动化和机器学习领域的从业者和研究人员提供了有价值的见解。
