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An approach to develop an arc sensor for gap-width estimation during automated NG-GMAW with a weaving electrode motion is introduced by combining arc sensor readings with optical measurement of the groove shape to allow precise analyses of the process. The two test specimen welded for this study were designed to feature a variable groove geometry in order to maximize efficiency of the conducted experimental efforts, resulting in 1696 individual weaving cycle records with associated arc sensor measurements, process parameters and groove shape information. Gap width was varied from 18 to 25 mm and wire feed rates in the range of 9 to 13 m/min were used in the course of this study. Artificial neural networks were used as a modelling tool to derive an arc sensor for estimation of gap width suitable for online process control that can adapt to changes in process parameters as well as changes in the weaving motion of the electrode. Wire feed rate, weaving current, sidewall dwell currents and angles were used as inputs to calculate the gap width. Evaluation the proposed arc sensor model show very good estimation capabilities for parameters sufficiently covered during experiments.
Over the past couple of decades Non-Destructive Testing (NDT) has seen a significant increase in the use of automation. In addition to increased reliability, objectivity, consistency, repeatability, productivity, and so on, automating parts of the process is expected to decrease the potential for human error. However, the literature on human-automation interaction suggests that automation is not only associated with benefits, but also with new risks and risk sources. First, this paper will present the methodology used to identify—for the first time—possible risks associated with mechanised data acquisition and corresponding data evaluation. Moreover, it will highlight possible risks, their causes, consequences, and ways of preventing them. Second, those preventive measures will be further analysed by examining new risks that can arise from their implementation, i.e. potential for failure that can arise from (a) working with automated defect-detection and sizing aids, (b) implementing human redundancy, and (c) improvement of the inspection procedures without due consideration of the procedure users. And third, some optimisations strategies will be provided. The purpose of this work is to show that mechanised testing is associated with potential for failure and that the sources of those risks go beyond single inspectors and need to be looked at in the interaction of people with other systems, i.e. the technology, the team and, most importantly, the organisation.