Journal
JOURNAL OF INTELLIGENT MANUFACTURING
Volume 32, Issue 3, Pages 895-912Publisher
SPRINGER
DOI: 10.1007/s10845-020-01645-3
Keywords
Face milling; Wear; Tool life; Tool condition monitoring; Flatness deviation; Cutting power; Random forest; SMOTE
Funding
- Act 211 Government of the Russian Federation [02.A03.21.0011]
- Ministerio de Economia Competitividad of the Spanish Government [TIN2015-67534-P]
- Junta de Castilla y Leon [BU085P17]
- European Union FEDER funds
- NVIDIA Corporation
Ask authors/readers for more resources
The study used the real-time tracking feature of CNC machines to predict flatness deviation and tested various machine-learning techniques. Random Forest ensembles combined with Synthetic Minority Over-sampling Technique (SMOTE) balancing technique showed the best performance when considering industrial requirements for discretized flatness levels. SMOTE balancing technique proved to be a useful strategy in overcoming limitations caused by small experimental datasets on the accuracy of machine-learning models.
The acceptance of the machined surfaces not only depends on roughness parameters but also in the flatness deviation (Delta(fl)). Hence, before reaching the threshold of flatness deviation caused by the wear of the face mill, the tool inserts need to be changed to avoid the expected product rejection. As current CNC machines have the facility to track, in real-time, the main drive power, the present study utilizes this facility to predict the flatness deviation-with proper consideration to the amount of wear of cutting tool insert's edge. The prediction of deviation from flatness is evaluated as a regression and a classification problem, while different machine-learning techniques like Multilayer Perceptrons, Radial Basis Functions Networks, Decision Trees and Random Forest ensembles have been examined. Finally, Random Forest ensembles combined with Synthetic Minority Over-sampling Technique (SMOTE) balancing technique showed the highest performance when the flatness levels are discretized taking into account industrial requirements. The SMOTE balancing technique resulted in a very useful strategy to avoid the strong limitations that small experiment datasets produce in the accuracy of machine-learning models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available