What is it about?
This research is an example of manufacturing analytics. It stems from a research project that focus on creating a fault detection system for metal cutting processes using process simulation models and data analytics. As part of this research project, in this paper we used the hybrid approach that combines physics-based simulations with machine learning tools such as Support Vector Regression, Random Forest Regression and Least Square Boosting in order to significantly improve milling force predictions, achieving up to 98% accuracy and demonstrating effective performance with minimal test data across various materials and cutting tools.
Featured Image
Why is it important?
We used simulation data to predict the milling force. This is among the first examples where data analytics and physical simulations are used for such a purpose.
Perspectives
I consider manufa.cturing as a fertile field where both data analytics and decision making tools can contribute much. This is an example of such contribution
Prof. Kemal Kilic
Sabanci Universitesi
Read the Original
This page is a summary of: Improving milling force predictions: A hybrid approach integrating physics-based simulation and machine learning for remarkable accuracy across diverse unseen materials and tool types, Journal of Manufacturing Processes, March 2024, Elsevier,
DOI: 10.1016/j.jmapro.2024.02.001.
You can read the full text:
Contributors
The following have contributed to this page
