English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This chapter deals with the detector of saturation level in the magnetic (iron) core of a welding transformer. It is based on an artificial neural network (ANN) and requires only the measurement of the transformer’s primary current. The saturation level detector could be the substantial component of a middle frequency resistance spot welding system (RSWS), where the welding current and the flux density in the welding transformer’s iron core are closed-loop controlled by two hysteresis controllers. The resistance spot welding systems, described in different realizations (Brown, 1987), are widely used in the automotive industry. Although the alternating or direct currents (DC) can be used for welding, this chapter focuses on the resistance spot welding system (Fig. 1) with DC welding current. The resistances of the two secondary windings R2, R3 and characteristics of the rectifier diodes, connected to these windings, can slightly differ. Reference (Klopčič et al., 2008) shows that combination of these small differences can result in increased DC component in welding transformer’s iron core flux density. It causes increasing iron core saturation with the high impact on the transformer’s primary current i1, where currents spikes eventually appear, leading to the over-current protection switch-off of the entire system. However, the problematic current spikes can be prevented either passively or actively (Klopčič et al., 2008). When the current spikes are prevented actively, closed-loop control of the welding current and iron core flux density is required (Klopčič et al., 2008). Thus, the welding current and the iron core flux density must be measured. While the welding current is normally measured by the Rogowski coil (Ramboz, 1996), the iron core flux density can be measured by the Hall sensor or by a probe coil wound around the iron core. In the case, the flux density value is obtained by the analogue integration of the voltage induced in the probe coil (Deželak et al., 2008). Integration of the induced voltage can be unreliable due to the unknown integration constant in the form of the remanent flux and the drift in analogue electronic components. The drift can be kept under control by the use of closed-loop compensated analogue integrator. An advanced, the two hysteresis controllers based control of the RSWS, where the current spikes are prevented actively by the closed-loop control of the welding current and flux

Artificial Neural Network Applied for Detecting the Saturation Level in the Magnetic Core of a Welding Transformer