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PurposeThis paper aims to identify the impacts of wire and arc additive manufacturing (WAAM) technology on the green supply chain management (GSCM) performance. Also, it intends to identify the most essential WAAM capabilities.Design/methodology/approachAn exploratory case study related to a metallurgical company using WAAM technology to repair metallic components was developed. A research framework to identify WAAM production capabilities and the different GSCM performance criteria was proposed based on the current state of the art. Primary qualitative data provided evidence for developing seven propositions relating WAAM capabilities to GSCM performance.FindingsThe paper provides empirical evidence relating to how WAAM production capabilities impact the different performance criteria of the GSCM performance. The results show that “relative advantage” and “supply-side benefits” are critical capabilities developed through WAAM. Furthermore, most of the capabilities regarding “relative advantage” and “supply-side benefits” promote a higher GSCM performance.Research limitations/implicationsThis research was carried out using a single case study research design and using qualitative data. Thus, future works are encouraged to test the propositions empirically using quantitative methodologies.Practical implicationsThe case study findings support that most WAAM production capabilities promote a higher GSCM performance. Managers could use this research to understand the capabilities developed by this fusion-based additive manufacturing (AM), become aware of the implications of new technology adoption on the supply chain environmental externalities, and develop new business models based on the WAAM capabilities.Originality/valueThis research contributes to expanding the state-of-the art related to WAAM technology by evidencing the relationship between adopting this fusion-based AM technology and green supply chain practices. Also, it provides a set of seven propositions that could be used to theorise the impacts of WAAM adoption on the GSCM performance.

The austenite grain size (AGS) before decomposition is a crucial factor for the development of microstructure. However, this dependency is seldom discussed due to the difficulty of observing the grain growth of austenite during welding. In the current work, a grain growth algorithm is combined in a thermodynamics-based metallurgical model for the first time to analyse the influence of prior austenite grain size (pAGS). The phase volume fractions predicted at different cooling rates and pAGSs are compared with the experimental results of the continuous cooling transformation (CCT) diagram. To further investigate the influences of pAGS and microstructure on residual stress, experiments of bead-on-plate welding are conducted at three heat inputs, in which plates of S700 steel are operated by the arc welding process. The geometries after welding, chemical composition in the fusion zone (FZ) and the parameters of the double ellipsoidal heat source are calibrated using the software SimWeld. These geometries are imported to ABAQUS to create a finite element (FE) model. The validated metallurgical model together with the grain growth algorithm are implemented in the subroutine ABAMAIN to provide a thorough prediction of microstructure. With the knowledge of temperature and phase distributions, a coupled thermo-metallomechanical FE model is established to predict the residual stress distributions. The material properties are assigned by interpolating the individual phase property with its volume fraction. By comparing the results predicted by the model assuming constant pAGS, the influence of the pAGS on the residual stress is manifested. Moreover, simulations using overall material properties are also conducted. The stress distributions in the middle of plate surface are plotted along with the volume fractions of product phases to analyse the sensitivity of the residual stress to microstructure.

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