Title | CDW aluminium joints welding and optimisation with NDT/mechanical testing |
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Publication Type | Articolo su Rivista peer-reviewed |
Year of Publication | 2019 |
Authors | Panella, F.W., Dattoma V., De Giorgi M., Palano F., and Carofalo A. |
Journal | International Journal of Advanced Manufacturing Technology |
Volume | 102 |
Pagination | 3689-3698 |
ISSN | 02683768 |
Keywords | Capacitor discharge welding, Charge density waves, Defects, Electrical-resistance welding, Fatigue of materials, Fatigue testing, Geometrical aspects, High cycle fatigue test, Hybrid capacitor, Nondestructive examination, Process characteristics, Residual stresses, Resistance welding, Supercapacitor, Technological parameters, Tensile testing, Ultrasonic inspections, Ultrasonic testing, Welds |
Abstract | This work investigates the possibility to apply the improved hybrid capacitor discharge welding (CDW) process, based on projection welding principles, to aluminium alloy Al 5754. The CDW process is an electrical resistance welding technology, realised with high-intensity current pulses discharged by large capacitors. The innovative aspect is the effective possibility to weld aluminium alloys with CDW process and improve the mechanical weld characteristics and the presence of defects as a function of the technological parameters; intrinsic CDW process characteristics need to be investigated on the basis of interaction between the technological and geometrical aspects and the related mechanical properties, in order to improve welding shape and reduce defect size. In order to optimise the process, visual and ultrasonic inspections of the most significant welded joints were performed, and residual stress values were checked; in addition, high-cycle fatigue tests after room temperature tensile tests were executed to optimise the weldments. © 2019, Springer-Verlag London Ltd., part of Springer Nature. |
Notes | cited By 0 |
URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061982772&doi=10.1007%2fs00170-019-03412-y&partnerID=40&md5=8443d8045f0c5624c5800677be4cad4b |
DOI | 10.1007/s00170-019-03412-y |
Citation Key | Panella20193689 |