Recent advances in HTS ReBCO tapes processing and performances have opened attractive perspectives for applications. For the development of HTS-based technology there are several technological challenges to be addressed such as cable terminations. In this contribution, the effect of load, temperature and joint process time on solder joint process of SuperPower SCS4050 tapes with the Pb-Sn solder paste, was investigated using a surrogate model. The prediction of contact resistance RSj = Rj × S (S is the joint contact area) as a function of manufacturing parameters is a complex and time-consuming multi-physical problem involving a coupling of mechanical, thermal and electrical equations. Therefore, a surrogate model was implemented to the aim of predicting the effects of parameters on the joint resistance, without developing a computationally intensive multi-physical model. Taking full advantage of the reduced number of tests performed, the surrogate model demonstrated to be useful to supply significant information for tests, allowing to focus directly on the most promising sets of parameters, avoiding an expensive trial-and-error experimental campaign. © 2014 IEEE.

Surrogate modelling to minimize contact resistance of HTS ReBCO terminations

Della Corte, A.;Augieri, A.;Celentano, G.;Tomassetti, G.
2014

Abstract

Recent advances in HTS ReBCO tapes processing and performances have opened attractive perspectives for applications. For the development of HTS-based technology there are several technological challenges to be addressed such as cable terminations. In this contribution, the effect of load, temperature and joint process time on solder joint process of SuperPower SCS4050 tapes with the Pb-Sn solder paste, was investigated using a surrogate model. The prediction of contact resistance RSj = Rj × S (S is the joint contact area) as a function of manufacturing parameters is a complex and time-consuming multi-physical problem involving a coupling of mechanical, thermal and electrical equations. Therefore, a surrogate model was implemented to the aim of predicting the effects of parameters on the joint resistance, without developing a computationally intensive multi-physical model. Taking full advantage of the reduced number of tests performed, the surrogate model demonstrated to be useful to supply significant information for tests, allowing to focus directly on the most promising sets of parameters, avoiding an expensive trial-and-error experimental campaign. © 2014 IEEE.
CICC;artificial neural network;joint;termination;Surrogate model
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/2125
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