Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning
Top Cited Papers
- 23 January 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Journal of Intelligent Manufacturing
- Vol. 33 (5), 1467-1482
- https://doi.org/10.1007/s10845-020-01725-4
Abstract
No abstract availableKeywords
This publication has 28 references indexed in Scilit:
- A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM)Robotics and Computer-Integrated Manufacturing, 2015
- Extreme learning machines: new trends and applicationsScience China Information Sciences, 2015
- Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approachSolar Energy, 2014
- Metal Additive Manufacturing: A ReviewJournal of Materials Engineering and Performance, 2014
- A multi-performance prediction model based on ANFIS and new modified-GA for machining processesJournal of Intelligent Manufacturing, 2013
- Surface roughness analysis, modelling and prediction in selective laser meltingJournal of the American Academy of Dermatology, 2013
- Application of adaptive network based fuzzy inference system method in economic welfareKnowledge-Based Systems, 2012
- SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environmentJournal of Hydrology, 2012
- Deposition of Ti–6Al–4V using laser and wire, part I: Microstructural properties of single beadsSurface and Coatings Technology, 2011
- Low cost integration of additive and subtractive processes for hybrid layered manufacturingRobotics and Computer-Integrated Manufacturing, 2010