Virtual surface morphology generation of Ti-6Al-4V directed energy deposition via conditional generative adversarial network

The core challenge in directed energy deposition is to obtain high surface quality through process optimisation, which directly affects the mechanical properties of fabricated parts. However, for expensive materials like Ti-6Al-4V, cost and time required optimise parameters can be excessive inducing good quality. To mitigate these challenges, we propose a novel method with artificial intelligence generate virtual morphology Ti-6Al-4V parts by given parameters. A high-resolution image generation system has been developed optimising conditional generative adversarial networks. matches experimental cases well an Fréchet inception distance score 174, range accurate matching. Microstructural analysis guidance exhibited less textured microstructural behaviour on reduces anisotropy columnar structure. This help high-quality cost-effectively.