6533b833fe1ef96bd129b8db
RESEARCH PRODUCT
Using a neural network for qualitative and quantitative predictions of weld integrity in solid bonding dominated processes
Livan FratiniGianluca BuffaGiuseppe Patrinostrosubject
business.product_categoryMaterials scienceArtificial neural networkMechanical EngineeringMetallurgyFriction Stir WeldingProcess (computing)Mechanical engineeringWeldingStrain rateNeural networkAluminum alloysComputer Science Applicationslaw.inventionRoll bondinglawModeling and SimulationDie (manufacturing)Friction stir weldingGeneral Materials ScienceExtrusionBonding criterionbusinessCivil and Structural Engineeringdescription
Solid-state bonding occurs in several manufacturing processes, as Friction Stir Welding, Porthole Die Extrusion and Roll Bonding. Proper conditions of pressure, temperature, strain and strain rate are needed in order to get effective bonding in the final component. In the paper, a neural network is set up, trained and used to predict the bonding occurrence starting from the results of specific numerical models developed for each process. The Plata-Piwnik criterion was used in order to define a quantitative parameter taking into account the effectiveness of the bonding. Excellent predictive capability of the network is obtained for each process.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-04-01 | Computers & Structures |