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The cladding welding analysis with pulsed flux cored arc welding (FCAW) process, were carried over a AISI 1020 base metal (thickness 12,7 mm, width 63,5 and length 185mm) with an CA6NM steel wire with diameter of 1.2mm. Was performed only one weld cord in the flat position. For experimental design was used the method of Taguchi L9 to determinate the parameter to be analyzed through the application of the analysis of variance (ANOVA) method. The response signals in RMS (Root Mean Square) analyzed were the voltage, current and acceleration. The procedure is based on a non-parametric domain-selective ANOVA for functional data, which results in the selection of the intervals of the domain presenting the most statistically significant effects of each factor over the selected response signals. The statistical results presented by ANOVA show that not all the selected variables have influenced the results. The best results for the cladding welding were obtained from the current average of230amperes, and statistically the average current was the variable that significantly affected the results, however, the welding speed only affected the yield of the process.

Regenerative Braking in vehicles is projected as an intelligent and simple technique to improve the energy replenishment and autonomy in them, due to the fact that it is based on the concurrent process of deceleration. It is smart because takes advantage of the capability of the electric machine to behave as a generator. So, it is possible to recover the kinetic energy store in the rotating inertias by using regenerative braking during the deceleration and restore it into batteries. Some authors are focused on maximizing the energy regenerated, applying techniques as independent factors of the process, like storage elements, control of speed, acceleration, current, etc. In this work we exposes the regenerative braking process and its main parts in a holistic model, where discrete and continuous states and events are related, also linked into the dynamic behavior, bringing the possibility not only to have a general model that commands all the process, but also improve and monitor each stage as a whole. Thus our management architecture pretends to maximize the gain energy in every deceleration, and minimize the energy consumed.

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