Innospace partners with Oqton and 3D Convergence Industry Association for AI-driven metal additive manufacturing for space launch vehicles

South Korean space launch vehicle developer Innospace has signed a three-party memorandum of understanding with global AI manufacturing software provider Oqton and the 3D Convergence Industry Association to advance AI and digital transformation in metal additive manufacturing. The agreement, announced on April 29, 2026, covers joint development of AI-driven process optimization for metal 3D printing, workforce training programs, and collaborative research on quality assurance and certification protocols. Specifically, the partners will work to expand the digital transformation platform for 3D printing and establish a global cooperation framework for metal additive manufacturing. Innospace CEO Kim Soo-jong stated the partnership aims to standardize manufacturing processes using AI and digital transformation while expanding global metal AM competitiveness.

This partnership places Innospace within a growing trend of launch vehicle developers integrating AI into their AM workflows to accelerate qualification and reduce per-part costs. The company, which has previously demonstrated 3D-printed rocket engine components using LPBF, is now seeking to embed AI-driven process control and defect detection directly into its production pipeline. Oqton brings a proven AI platform for manufacturing execution and process optimization, while the 3D Convergence Industry Association provides industry coordination and standards development. This move mirrors similar initiatives by Relativity Space and Rocket Lab, both of which have invested heavily in AI-augmented AM to shorten development cycles and improve first-pass yield on critical engine parts. For Innospace, the partnership addresses a specific bottleneck: transitioning from prototype-level printing to repeatable, qualified production of metal components for orbital-class vehicles.

From a practical standpoint, this MOU is a signal that Innospace recognizes the gap between having AM capability and achieving production-grade repeatability. The company must now move from the agreement to concrete milestones: integrating Oqton's AI models into its existing LPBF workflow, establishing baseline defect rates, and demonstrating improved yield on a specific engine component. For the broader industry, this is another data point that AI-driven process control is becoming a prerequisite for AM adoption in aerospace, not a differentiator.