Prediction of the Outcome of the Welded Bead Bending Test (WBBT) using Machine Learning (ML) uri icon

Abstract

  • The WBBT, as described in Stahl-Eisen-Prüfblatt (SEP) 1390 [1], is used to demonstrate that structural steels intended for use in safety-critical applications, e.g. those erected in accordance with ZTV-ING Part 4 [2], have a sufficiently high crack arrest capacity. Three possible test results are distinguished: passed (p) = α ≥ 60 ° without fracture; not passed (n.p.) = fracture at α < 60 °; invalid = no crack recognisable in the base material after bending until α ≥ 60 °. In order to reduce material usage, emissions, and the time required for testing, this study proposes the use of machine learning (ML) to predict WBBT results.