6533b7ddfe1ef96bd1275353

RESEARCH PRODUCT

Weld quality prediction in linear friction welding of AA6082-T6 through an integrated numerical tool

Davide CampanellaLivan FratiniGianluca BuffaSergio Pellegrino

subject

0209 industrial biotechnologyEngineeringAluminum alloyField (physics)Interface (computing)Neural Network02 engineering and technologyWeldingIndustrial and Manufacturing Engineeringlaw.invention020901 industrial engineering & automationQuality (physics)lawFriction weldingSettore ING-IND/16 - Tecnologie E Sistemi Di LavorazioneFEMArtificial neural networkbusiness.industryOscillationMetals and AlloysStructural engineering021001 nanoscience & nanotechnologyFinite element methodComputer Science ApplicationsModeling and SimulationCeramics and CompositesLinear Friction Welding0210 nano-technologybusiness

description

Abstract A numerical and an experimental campaign were carried out with varying oscillation frequency and interface pressure. The local values of the main field variables at the contact interface between the specimens were predicted by a Lagrangian, implicit, thermo-mechanical FEM model and used as input of a dedicated Neural Network (NN). The NN, integrated in the FEM environment, was designed in order to calculate both a Boolean output, indicating the occurrence of welding, and a continuous output, indicating the quality of the obtained solid state weld. The analysis of the obtained results allowed three different levels of bonding quality, i.e., no weld, sound weld and excess of heat, to be correctly identified and predicted.

10.1016/j.jmatprotec.2016.01.012http://hdl.handle.net/10447/168806