On the efficiency of machine learning for fatigue assessment of post-processed additively manufactured AlSi10Mg
Authors:
- E. MalekiEME. Maleki
- S. BagherifardSBS. Bagherifard
- Nima RazaviNRNima Razavi
- M. BandiniMBM. Bandini
- A. du PlessisAPA. du Plessis
- F. BertoFBF. Berto
- M. GuaglianoMGM. Guagliano
- 14 March 2022
- journal article
- research article
- Published by Elsevier in International Journal of Fatigue
- Vol. 160
Abstract
No abstract availableKeywords
This publication has 50 references indexed in Scilit:
- Additive manufacturing technologies: state of the art and trendsInternational Journal of Production Research, 2015
- Modeling of Cathode Pt /C Electrocatalyst Degradation and Performance of a PEMFC using Artificial Neural Network2015
- Formulation of bead width model of an SLM prototype using modified multi-gene genetic programming approachThe International Journal of Advanced Manufacturing Technology, 2014
- Analysis of Laser Cladding Bead Morphology for Developing Additive Manufacturing Travel PathsProcedia CIRP, 2014
- Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysisJournal of Intelligent Manufacturing, 2012
- Balling behavior of stainless steel and nickel powder during selective laser melting processThe International Journal of Advanced Manufacturing Technology, 2011
- Surface Morphology in Selective Laser Melting of Metal PowdersPhysics Procedia, 2011
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006
- An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated dataEcological Modelling, 2004