Pages that link to "Item:Q1693809"
From MaRDI portal
The following pages link to Coping with complexity when predicting surface roughness in milling processes: hybrid incremental model with optimal parametrization (Q1693809):
Displaying 9 items.
- Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining (Q400193) (← links)
- Regression and ANN models for estimating minimum value of machining performance (Q437930) (← links)
- Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness (Q534860) (← links)
- Texture prediction of milled surfaces using texture superposition method. (Q1400722) (← links)
- A note of hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining (Q2363886) (← links)
- Evolutionary support vector regression algorithm applied to the prediction of the thickness of the chromium layer in a hard chromium plating process (Q2396453) (← links)
- A combined finite element and machine learning approach for the prediction of specific cutting forces and maximum tool temperatures in machining (Q2672193) (← links)
- A Bayesian network model for surface roughness prediction in the machining process (Q3612774) (← links)
- Prediction of mill liner shape evolution and changing operational performance during the liner life cycle: Case study of a Hicom mill (Q5306439) (← links)
