Prediction of surface roughness based on fusion model
Abstract
Accurate prediction of surface roughness based on input cutting parameters is beneficial for controlling the surface quality of the workpiece. Traditional mathematical models struggle with the complex relationship between cutting parameters and surface quality. The objective of the research is to identify an optimal model and hyperparameters through a comprehensive evaluation process. To achieve desired surface roughness, a fusion model is presented to control production cutting parameters. Experimental results indicate that fusion models trained with machine learning algorithms are highly accurate in predicting the surface roughness of additively manufactured parts. Notably, the data fusion method enhances prediction accuracy even further.
Fields of Study & Subjects
Surface Roughness and Optical Measurements
Advanced machining processes and optimization
Adhesion
Friction
and Surface Interactions
Publication details
- Journal / source
- Procedia CIRP
- Publisher
- Elsevier BV
- Volume
- 138
- Pages
- 568–572
- Publication date
- 1 January 2026
- Language
- EN