An experimental investigation and neuro-fuzzy modeling to ascertain metal deposition parameters for the wire arc additive manufacturing of Incoloy 825

Abstract

Additive manufacturing is critical to repair, rebuild, and rapidly manufacture major components in today's manufacturing environment. Wire arc additive manufacturing technique using metal inert gas welding, which is a better way to produce large-scale parts at a subsidized cost was experimented on Incoloy 825 due to its many industrial applications in the construction of large structures. As bead deposition is vital for wire arc additive manufacturing, single bead depositions were performed. Metrological features of all deposited beads are evaluated using the CNC Video Measuring System by capturing the macrostructures. The effects of input parameters on bead geometry are statistically analyzed, and regression models are derived to forecast bead geometry. An adaptive Neural Fuzzy Inference System was implemented to predict geometrical characteristics, and the results were compared with experimental results. Subtractive clustering fuzzy inference model obtained the lowest percentage error difference in predicting deposited bead geometry as a function of process parameters. Further, the multi-objective optimization technique determined optimal input parameters for the best bead geometry. Finally, using identified optimum parameters, wire arc additive manufacturing of a single-pass multi-layer wall of Incoloy 825 was successfully constructed for validation, which revealed that the microstructure of the built structure was of appreciable quality without cracks and porosity.