• Salvadó, Laura Laguna
• Terrenoir, Laurent
• Merlo, Christophe
• Lartigau, Julie
• Arjunan, Arun

Abstract

Directed Energy Deposition (DED) processes are not widely used in industry. To contribute to their integration, it appears essential to handle both the quality of the produced parts and the sustainability of the manufacturing process. This paper presents a methodological framework to support the implementation of these technologies through the choice of relevant manufacturing parameters according to the targeted performances. It combines a genetic algorithm, suited to the optimisation of manufacturing parameters, the Nondominated Sorting Genetic Algorithm II (NSGA II) with a multi-criteria ranking algorithm. Thus, it brings the necessary interaction with the decision-maker, a high computational power and a simplicity of use for the decision maker. The proposed model has been developed for the optimisation of manufacturing parameters of the Wire Arc Additive Manufacturing (WAAM) process according to both mechanical characteristics (load bearing capacity and dimensional accuracy) and industrial criteria (cost and environmental impact). As this model aims to participate to the industrialisation of DED processes, it has therefore been designed to be transferable to a broad range of cases integrating various geometries, materials and DED processes.

Topics

• Deposition
• wire
• directed energy deposition