Selecting a welding process for a given application is crucial with respect to the sustainability of part manufacturing. Unfortunately, since welding processes are evaluated by a number of criteria, preferences for one or the other process can be contradictory. However, the prevalent procedure of weight assignment for each criterion is subjective and does not provide information about the entire solution space. From the perspective of a decision maker it is important to be able to assess the entire set of possible weightings and answer the question which welding process is optimal for which set of weights. This issue is investigated by means of a weight space partitioning approach. Two welding processes are considered with respect to three criteria that reflect their economic and environmental performance. In order to find the most sustainable welding process the underlying weight space partition is evaluated.
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.