Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations

The prediction of machining processes is a challenging task and usually requires a large experimental basis. These experiments are time-consuming and require manufacturing and testing of different tool geometries at various process conditions to find optimum machining settings. In this paper, a mach...

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Bibliografische gegevens

Hoofdauteurs:	Hagen Klippel, Eduardo Gonzalez Sanchez, Margolis Isabel, Matthias Röthlin, Mohamadreza Afrasiabi, Kuffa Michal, Konrad Wegener
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT 2022-03-01
Reeks:	Journal of Machine Engineering
Onderwerpen:	ti6al4v sph machining machine learning
Online toegang:	http://jmacheng.not.pl/Cutting-Force-Prediction-of-Ti6Al4V-using-a-Machine-Learning-Model-of-SPH-Orthogonal,147201,0,2.html
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